《人工智能小白书》（第 1 部分 · 第 1–12 章）

The Little Book of AI (part 1, chapters 1–12)

> By Li Xiaolai · his book manuscript published on GitHub (his own Chinese and English versions) · [GitHub repository](https://github.com/xiaolai/little-book-of-ai) · version of 2026-10-11. Images are given as links.

Foreword

About two hundred years ago, our world went through a seismic shift: the Industrial Revolution.

One "side effect" of this revolution was a general decline in human physical strength. Suddenly, people didn't need to engage in the kind of back-breaking labor that was once the norm. The result? A noticeable and undeniable drop in physical prowess for most folks. Fast forward a few generations, and if we could hop into a time machine back to those days, we'd be the weaklings of the past — the ones who couldn't even wring a chicken's neck.

By the 1960s, thanks to technological advances, abundant resources, improved healthcare, and better nutrition, another trend emerged. People started living longer. Life expectancy jumped from under 30 years in the late 19th century to nearly 80 by the early 21st century. But longer life didn't necessarily mean healthier life. Compare a typical beach photo from 1960 with one from 2023:

[〔图〕](https://github.com/xiaolai/little-book-of-ai/blob/70679bd098497a49664f6489ccec10f88bb27109/en/images/1960-2023-beach.png)

On one end of the spectrum, most people's physical strength has taken a nosedive. On the other? There's a group of folks who've gained superhuman strength through "scientific training."

As modern humans, if you can crank out 10 push-ups in one go, you're probably in the top 20%. Sure, ancient folks might've had better overall stamina, but I doubt many could do 100 push-ups straight. Guess how many the world record holder can do now? In 1980, Japan's Minoru Yoshida did 10,507 push-ups non-stop. In 1993, Charles Servizio from the U.S. did 46,001 push-ups in 21 hours and 21 minutes.

It's not just about breaking "physical limits." More importantly, even though the percentage of people training is low, the absolute number of those with strong physical capabilities is on the rise.

In 1908, the Olympic marathon record was 2 hours, 55 minutes, and 18 seconds. Fast forward over a century, and the new record is about 2 hours, 2 minutes, and 57 seconds — shaving off over 50 minutes, a 30% reduction. A hundred years ago, only one person could run a marathon in under 2 hours and 55 minutes. Now? Around 30,000 runners qualify for the Boston Marathon each year, all beating the 3-hour, 5-minute mark.

This is how the once "earth-shattering" Industrial Revolution impacted human "physical strength":

> * Most people's physical strength has declined (that's a fact).
> * The percentage of people who exercise regularly has dropped (say, from 20% to 2%).
> * But among those who do exercise, a larger proportion achieve higher results than before (the success rate has increased, say, from 20% to 80%).
> * The best performances among exercisers are now unimaginable in the past (like 46,000 push-ups).
> * The gap between the first and second place keeps widening (for example, the second might only manage 5,000 push-ups).

Now, let's use this as a reference to imagine what the "sudden emergence" and "rapid development" of artificial intelligence might do to human "brainpower."

Just as the Industrial Revolution reduced the physical demands for survival, the mental demands for survival have been decreasing too. This isn't unique to the AI era; it's been a trend for years. One reason for the booming consumer society over the past 30-50 years is the widespread adoption of the "idiot-proof design principle" — products that don't require learning or thinking to use are the ones with the potential to sell big.

Now, this trend is about to be amplified by AI — because AI lowers the "difficulty of all learning." In other words, even if you have to learn or think, AI drastically reduces the cost of doing so. The evolution from "personal computers" to "mobile computers" to "smart mobile devices" is a prime example. Devices are getting lighter, smarter...

We can't say people are getting dumber, but it's true that "devices are getting simpler, easier to use, and more intuitive." Computers once had a steep learning curve. Now? While truly mastering them is getting harder, for most people, "idiot-proof smartphones" are not only sufficient but feel increasingly user-friendly.

So, the "current AI revolution's impact on human brainpower" might parallel or align with the "Industrial Revolution's impact on human physical strength":

> * Most people's brainpower is declining.
> * The percentage of people who engage in self-directed learning is dropping.
> * Among those who do learn, a larger proportion achieve higher results than before.
> * The best performances among learners are now unimaginable in the past.
> * The gap between the first and second place keeps widening.

In fact, this trend was already in motion before AI "suddenly appeared" — human brainpower has been on the decline, with the weak getting weaker and the strong getting stronger.

In 2015, India's Rajveer Meena became the recognized record holder by reciting the first 70,000 digits of π, taking 9 hours and 7 minutes. Japan's Akira Haraguchi claims to have recited an even more incredible 100,000 digits, nearly 200 times what anyone could do 42 years ago. Another example: India's Vikas Sharma can calculate the roots of 12 large numbers, each with 20 to 51 digits, in under a minute, faster than many can type the numbers into a calculator and read the answer.

"Remembering the first 70,000 digits of π" or "quickly calculating the 50th root of a 51-digit number" are mind-blowing examples of "super brainpower," much like "46,001 push-ups" show physical limits being shattered. But these examples often feel distant to most "ordinary folks," as if they have nothing to do with them.

Let's look at some more relatable facts. In the 1990s, scoring over 600 on the 677-point TOEFL exam in mainland China was rare, with most people struggling to hit 550. Now, with a new TOEFL scoring system out of 120, scores of 116 and above are common (equivalent to about 657 in the old system). Not only that, but the proportion of middle schoolers achieving high scores keeps rising.

The weak get weaker, the strong get stronger.

The trend is there.

What's left is the individual's choice within that trend...

— What's your choice?

<div style="text-align: right;">
Xiaolai<br />October 2024, Beijing
</div>

01. An AMA Bot

[〔图〕](https://github.com/xiaolai/little-book-of-ai/blob/70679bd098497a49664f6489ccec10f88bb27109/en/images/dalle/01.png)

1.1. ChatGPT: The Ultimate AMA Bot

What exactly is ChatGPT? We could define it in all sorts of ways and explore its existence from multiple angles, but let's start with a "comparison definition" — it's probably the quickest way to get the picture.

An "AMA" stands for "Ask Me Anything," which pretty much means you can throw any question my way, and I'll do my best to give you a solid answer.

Back in 2009, Reddit launched this section called "[r/IAmA](https://en.wikipedia.org/wiki/R/IAmA)." It was like a digital Woodstock for curiosity, where anyone could grill special guests who were ready to take on any query. This section took off like a rocket, growing its fan base and roping in big shots like Obama, Bill Gates, and Elon Musk to join the fun. But as ready as these celebrities were, they couldn't really spill the beans about everything given their limited time and scope of expertise, not to mention their willingness.

For someone asking the questions, even with a tag like "Ask Me Anything," there's still a cap on how far one can go. Social etiquette keeps you from asking Bill Gates, for instance, “Hey, how do I fix the blue screen on Windows?” That’d be kind of awkward, right?

More often, the biggest snag is simply their tight schedules. The more you’ve got on your plate, the less time there is to spare, which means they’re not always around to chat. Plus, lots of people we'd love to quiz aren’t even alive anymore.

> - Hey, Steve Jobs, does "Stay simple, stay foolish" still hold up in today's fast-paced AI world?
> - Martin Luther King Jr., what's your take on Trump's presidency?
> - George Washington, was there really a cherry tree in your front yard when you were a kid?
> - Nikola Tesla, with your unique way of thinking, what tech would you be itching to invent right now?
> - Charles Dickens, do your reflections on revolution in "A Tale of Two Cities" still resonate in today's world?

So, when it comes down to it, we’re often stuck with fascinating questions and no good way to get answers.

Here’s where ChatGPT comes in as your go-to "AMA bot." Whenever you're itching for an answer, it's got a response, anytime, day or night. No topic's off the table, no question too out there. It can even slip into different roles, free from the chains of time. It’s almost like a time machine, hopping across eras and scenarios, letting you dive into endless worlds of conversation with anyone you can imagine.

> - Act as Martin Luther King Jr.. How would you evaluate Trump's presidency?
> https://chatgpt.com/share/6716e439-ac60-8009-aa72-0db1d819b11d
> - Be honest as Gorge Washington. Was there really a tree in your front yard when you were a kid?
> https://chatgpt.com/share/6716e478-67a0-8009-b385-9cc69a8e2f9b

1.2. From Google to ChatGPT

Let's be real — ChatGPT isn't the pioneer of AMA bots. In a way, isn't the whole internet an AMA bot?

Early on, back in 1998, we got Google — the closest thing to an AMA robot we had at the time and still the most popular search engine around. Google burst onto the scene and changed the game by letting us ask it anything. It offers just a simple text box for us to type into, and all we had to do, and still do, is chat it up or pose a question. You type something in, and Google hits you back with thousands of related web pages, helping you dig up the answers you need.

From this angle, Google kind of nailed the basic AMA bot vibe.

> - **Anyone can ask anything**: Whether it's a deep academic question or just everyday stuff, Google’s got your back with a response.
> - **Vast information retrieval**: Google doesn't just spit out answers. Instead, it points you to web pages or resources related to your query, helping you find potential answers from the vast ocean of the internet.
> - **24/7 availability**: Just like today's AMA bots, Google is always on, ready to help anytime, anywhere, 24/7.

Nevertheless, here's the rub — Google's a search engine, not a know-it-all. It can't actually whip up answers by itself. It doesn't "get" what you're really asking and can't stitch things together to serve up a neat, concise reply. Picture it as a guide in a gigantic shopping mall. Super handy? Yes. Always right on the money? Not exactly. It'll point you in the general direction, but finding the right store might mean wandering around a bit — or worse, ending up on a wild goose chase.

1.3. The Allure of ChatGPT

OpenAI got its start in 2015, but it was their release of GPT-3 in June 2020 that really turned heads.

This model was packing some serious heat with its whopping 175 billion parameters, making it the world heavyweight champion in language models at the time. With this kind of firepower, GPT-3 could juggle tasks like a pro, from pulling off smooth conversations and translating languages to penning down precise writing and even cranking out code. It was like a mega-leap forward for AI in understanding and generating human language. By March 2022, OpenAI rolled out a souped-up version called GPT-3.5, though people outside the tech bubble still mostly scratched their heads when they heard the name OpenAI.

Fast forward another six months, and ChatGPT, built on GPT-3.5, landed on the scene on November 30, 2022. This “generative AI for the everyday user” took just five days to amass a million users — a growth spurt that was totally out of left field.

[〔图：Time taken for major platforms to reach 1 million users〕](https://github.com/xiaolai/little-book-of-ai/blob/70679bd098497a49664f6489ccec10f88bb27109/en/images/time-taken-to-reach-1-million-users.webp)

| ChatGPT | 2022 | 5 days |
| ---------------- | ---- | ---------- |
| Instagram\*\*\* | 2010 | 2.5 months |
| Spotify | 2008 | 5 months |
| Dropbox | 2008 | 7 months |
| Facebook | 2004 | 10 months |
| Foursquare\*\*\* | 2009 | 13 months |
| Twitter | 2006 | 2 years |
| Airbnb\*\* | 2008 | 2.5 years |
| Kickstarter\* | 2009 | 2.5 years |
| Netflix | 1999 | 3.5 years |

> \* 1 million backers · \*\* 1 million nights booked · \*\*\* 1 million downloads

Just two months later, by January 2023, user numbers soared past 100 million. By the start of 2024, over 200 million folks were on ChatGPT. In the U.S. alone, ChatGPT Plus had roped in around 2 million paid subscribers, with the worldwide number sitting close to 18 million. By 2024, OpenAI was raking in an annual revenue of around $3.7 billion.

So, what’s making ChatGPT so magnetic to the average Jane and Joe? It's all about being “easy as pie.” The interface is dead simple — anyone can get it, no sweat. Just a text box and a send button. That’s it.

This kind of AMA bot had morphed into something anyone could access — no Ph.D. required. After its initial rollout in November 2022, the brains behind ChatGPT kept getting better and better. They upped it to GPT-4 in March 2023, then rolled out GPT-4o in May with “o” standing for “omni,” meaning a jack-of-all-trades among models. Come July, and GPT-4o mini was launched. On September 12, 2024, the preview of GPT-o1 dropped, showing off some serious upgrades in AI’s reasoning chops. It could now outthink even grad schoolers on some tests, especially in fields like physics, chemistry, and biology. The pace at which AI is evolving is way beyond what any futurist could’ve dreamt up.

But for the everyday user, it really boils down to that uber-simple interface: “one text box + one send button” and you’re off to the races. Easy peasy, and it’s getting brains faster than you can blink.

ChatGPT today has practically graduated into a “near-perfect AMA bot.” We can hit it up any time, anywhere, with any question, and it spits out answers like it’s nothing — from no-brainer facts to deep tech dives. Unlike old-school search engines, ChatGPT doesn’t just throw data at you; it weaves the info together, giving you polished, straightforward answers. It’s got your back with factual questions, writing projects, coding, even study tips. Now, AMA bots like ChatGPT are shaking up how millions around the world find info and chat it up in real-time.

At the very least, I can toss ChatGPT that awkward question I’d never ask Bill Gates in person: “How do I fix the Windows blue screen?” ChatGPT comes back with a bang-on answer — no awkwardness, no judging, just straight to the point.

> Hey, Bill Gates, how do I fix the Windows blue screen?
>
> https://chatgpt.com/share/6716e79a-303c-8009-8728-5e5f18d6f444

If Google feels like a “giant library,” ChatGPT is more like a “genie” that’s popped up inside an even bigger, top-notch library. It’s not just organizing and expanding it but also gaining a sharper understanding of everything on the shelves. And as an AMA bot, it’s all ears, ready to chat with anyone at any moment.

1.4. Speaking Isn’t So Simple

Let’s chat about something intriguing. When you think of Google or ChatGPT, they’re pretty much the epitome of simplicity. Just type in your thoughts and boom!

But hold on a sec. If I asked you, "Do you actually know how to speak?" you'd probably feel a little off, right? No worries, I’m not throwing shade, because I know you definitely agree with this:

> Plenty of folks just can't speak apropriately or communicate effectively .

At the same time, in this context, my earlier "question" should be precisely rephrased as:

> Do you really know how to effectively communicate with an increasingly smart AMA bot?

Let's be real, many folks start their conversation with ChatGPT with a simple "Hello." Seriously, this happens way more than you'd think. A ton of people dive into their interactions with these exact words. Even you might well have thrown that greeting out there at least once.

But, you know what? That’s kind of a head-scratcher. It’s like wasting your time and giving the AI a non-essential job. Unlike us, AI doesn't run on emotions or social norms. It doesn't get the whole greeting ritual — no warm fuzzies or need for pleasantries.

ChatGPT is all about its algorithms. It kicks off interactions based on direct tasks or questions. So, when you type "Hello," it’s just itching to decode that as "give me the task." And then you’re left staring at the screen, waiting for a follow-up. Pretty much a wild goose chase, right?

Take Google, which has been around the block since 1998. By 2024, that's 26 years! With its no-frills interface — just a search box and a button — how have people been handling it? Believe it or not, even now about 90,500 folks each month hit Google with a basic question. Talk about a head-scratcher!

> Am I pregnant?

And the fun part? Google’s got this cool feature, "smart suggestions," which guesses your next move when you're typing away. It taps into a treasure trove of data from previous queries, so it lines up your possibilities, giving you the most asked stuff upfront.

[〔图：am-i-pregnant-google.png〕](https://github.com/xiaolai/little-book-of-ai/blob/70679bd098497a49664f6489ccec10f88bb27109/en/images/am-i-pregnant-google.png)

1.5. Understanding the Limits of Tools

Let's dive into an interesting observation: why is it that when people start chatting with ChatGPT, they often say "Hello!"? And why do some folks ask Google question like "Am I pregnant?" or "How do I get home?"

These quirks share a common thread: people tend to use tools without really considering their limits first. If you're grabbing a helpful tool, its boundaries should be clear before you start using it — and ideally, you should think this through before diving in.

Take Google, for instance. It's a search engine that combs through existing online materials, fetching content related to your "search keywords." Once it pulls the info, sorting through and analyzing it is all on you. Google's job is pretty much figuring out the relevance between "online content" and "your search keywords" — and that's its boundary. Beyond that, it's out of its depth.

So, when someone asks Google, "Am I pregnant?" there's really just one keyword — "*pregnant*." Even if Google flexes all its muscles, it can only find things about being "*pregnant*." The "*I*" isn't a keyword, and Google doesn't know who "*I*" is. If users had considered Google's boundaries beforehand, they wouldn't ask such questions.

Similarly, if someone greets ChatGPT with "Hello!" first, it's clear they haven't thought about what ChatGPT can or can't do. Being polite is great, but that's not ChatGPT's gig. Using "please" or being courteous doesn’t add value; it's like throwing money, tokens, electricity, time, and energy down the drain with each chat.

Now, here's a question worth chewing over: How can we truly understand a tool’s limits? It's a challenge to think of everything right from the start, but it's a good problem to mull over. Make space in your notebook to jot down thoughts and revisit them often. At the very least, we know this: a bit of reflection helps.

> Sometimes, I get bored, and I'll set up a role for ChatGPT: "Play a foul-mouthed but sharp and super capable assistant. You know swearing won't tick me off, but uncreative swearing will make me think you're a bit of a doofus..."

Here's a thought-provoking question:

> As a flexible, efficient, and pretty smart tool, where do the boundaries lie for generative AI like ChatGPT?

Clearly, it's tough to cover all the bases in one go. But it's a solid question, worth kicking around and revisiting. It's the kind of thing you want to jot down in your notebook, leaving plenty of room for updates and tweaks.

At least we know one thing for sure:

> * It can't handle human emotions or feelings.

With a bit more thought, we can add to that:

> * It lacks empathy or compassion.
> * It can't make moral judgments.
> * It can't perceive the external environment.

You could even ask ChatGPT directly about its limitations and boundaries:

> Be honest with me, what are ChatGPT's limitations? Where do its boundaries lie?
> https://chatgpt.com/share/6716eb76-e6e4-8009-8b89-e73c1c254abd

ChatGPT is pretty upfront and gives a detailed answer:

> Here’s an honest breakdown of ChatGPT’s key limitations and boundaries:
>
> 1. **Knowledge Cutoff**:
> ChatGPT's training data cuts off at September 2021 (or October 2023, depending on the version). Anything that has happened after this date — current events, new scientific discoveries, updated laws, etc. — is unknown to me unless I use browsing tools to fetch up-to-date information.
>
> 2. **Real-time Updates**:
> While I can fetch some real-time information via browsing, I don’t have live access to all websites, databases, or private sources of data. This limits my ability to retrieve information from behind paywalls or certain proprietary systems.
>
> 3. **Reasoning and Problem Solving**:
> I can handle basic reasoning and problem-solving tasks, but I’m not capable of deep abstract thinking or complex long-term planning like humans. Tasks that involve deep creativity, nuanced judgments, or intricate emotional understanding may fall outside my capabilities.
>
> 4. **Context Retention**:
> I can retain information for the duration of a conversation and recall some persistent information through saved context. However, I cannot access detailed past conversations unless explicitly provided. There’s also a limit to how much context I can hold at once, meaning very long conversations can lead to loss of earlier details.
>
> 5. **Emotion and Empathy**:
> I can simulate empathy or emotion by using learned patterns of language, but I don’t actually "feel" anything. My responses are derived from patterns found in the data I was trained on. While I can be helpful in offering emotional support, this is fundamentally different from genuine human empathy.
>
> 6. **Understanding Complex, Dynamic Contexts**:
> I struggle with highly nuanced, constantly evolving, or ambiguous contexts, like subtle forms of humor, sarcasm, or complex social dynamics. I may miss key subtleties or provide interpretations that are overly literal.
>
> 7. **Interpretation of Instructions**:
> I interpret instructions based on patterns and examples from my training data, but this doesn't mean I can perfectly adapt to every unique set of user needs. For highly specialized requests, I might give a satisfactory answer but sometimes may misinterpret what’s being asked.
>
> 8. **Bias in Outputs**:
> Despite extensive mitigation efforts, I can still reflect biases present in the data I was trained on. This can manifest in gender, cultural, racial, or ideological biases. I’m not always neutral or objective in my responses because my data reflects the views and opinions found in the internet's vast textual corpus.
>
> 9. **Creativity Limitations**:
> While I can generate creative content, like stories, poems, or artwork (through tools), I’m bound by patterns I learned. Truly novel or breakthrough creative thinking is beyond my capabilities, and my "creativity" is often based on reassembling existing concepts in new ways rather than inventing from scratch.
>
> 10. **Ethical Boundaries and Restrictions**:
> I am constrained by ethical guidelines to avoid certain types of content. I won’t provide instructions for harmful activities, I won’t generate graphic or violent content, and there are strict limitations on generating content related to certain sensitive topics like self-harm, violence, or illegal activities.
>
> 11. **Task Performance**:
> For tasks that require human-like physical abilities (e.g., sensory experiences, motor tasks), I have zero capability. Similarly, while I can assist with tasks that involve numbers, words, or code, I can’t execute real-world decisions or actions.
>
> These are the general boundaries of my abilities. Each limitation represents areas where I aim to assist as best I can within the given framework.

If you ask ChatGPT the same question, you might get a slightly different answer — that's the "quirk" of generative AI, or rather, it's just how it works.

> [!Note]
>
> Throughout this book, I've mostly used GPT-4o, not GPT-4o-mini. As of the first draft of this book (up to October 2024), GPT-4o was the better and pricier model. Free users of ChatGPT are likely using a less advanced model, possibly still GPT-3.5...

1.6. The Tool Methodology

Actually, here's the most important takeaway for this chapter:

> **From the get-go, we need to create a comprehensive "Tool Methodology" for ourselves.**

Clearly, "thinking about the boundaries of a tool" is part of this methodology. Whatever tool you get your hands on, you need to seriously tackle a seemingly simple yet systematic set of questions:

> * Within the boundaries: What can it do?
> * Beyond the boundaries: What can't it do?

Going further, within those boundaries, we can dig deeper:

> * Within the boundaries: What can it do?
> * Basics: How do you use it?
> * Intermediate: How do you use it better?
> * Advanced: How do you use it best?

This is a habit.

It's a great habit.

Yet, it is a very simple habit.

This isn’t just a habit; it's a killer habit. Although it feels basic, it's crucial. Sadly, many folks out there have never developed this habit. They have no clue about its importance, let alone the setbacks they've faced due to this missing piece.

Think about it. Ever cracked open an English dictionary? Sure, you have. But have you ever thought about its limits? I mean, take phonetics symbols for eaxample. They might not exactly match how words roll off the tongue in real conversations. It’s like learning the user manual but not the real-world deal. You might’ve stumbled countless times without knowing why.

And what about cars? Everyone’s got one these days, but how many drivers really dive into understanding basic car stuff? It's pretty straightforward. If more folks did, we’d probably see at least half the traffic accidents disappear. That’s not an exaggeration. And guess what? As a society, we pay for these oversights in cash and tears, like some recurring subscription of grief. Always have, always will.

It’s mind-boggling that such basic, indispensable content isn't part of the "formal education system." Why’s it gotta be such an enigma?

From another perspective, if you’re a parent, wouldn’t it be smart to instill this in your kids ASAP? Do you need a Ph.D. to get it? Obviously not. This principle's simple but mighty, and even a kid can catch on — provided someone guides them. So yeah, keep hammering it home. But seriously, why are so many people blissfully unaware? Blame it on who? You gotta find a way to slip this checklist to your kiddo. It’s critical. If you drop the ball, you'll kick yourself down the line.

This stuff looks straightforward and it is, but that's exactly why folks brush it off. Parents skip it, teachers don’t bring it up, and schools don’t test it. Result? People don't know they don't know. Yeah, ignorance might be bliss until it bites you. But getting bitten without figuring out why? Now, that’s a raw deal.

Next up, consider this your "assignment." Dive into it because it'll help you (or your kid) nail down "Tool Methodology" from scratch. So far, we’ve barely scratched the surface talking boundaries. Of course, we'll be layering this "Tool Methodology" concept as we move forward, but for now, this exercise zeroes in on "boundaries."

> Take a fresh, hard look at every tool you might use...
>
> * Think about the limitations of these tools and where their boundaries lie.
> * Observe the people around you — do they have a systematic "tool methodology"?
> * Has anyone done something obviously ridiculous with a tool because they didn't understand its limits and boundaries?

Here are a few cases I've personally witnessed — my answers to that third question:

> * Trying to pry a nail with a screwdriver.
> * Attempting to cut metal with scissors.
> * Using navigation to find a car in a mall's underground parking lot.
> * Heating coffee in a metal cup in the microwave.
> * Trying to vacuum up liquids.
> * Standing still on a moving walkway at the airport.
> * Asking ChatGPT to diagnose a medical condition...

This list goes on and on. Writing down these scenarios isn’t about normalizing mistakes or poking fun. It’s to drive home how pivotal it is to "grasp a tool's limits and usage boundaries." For me, there's also an additional angle...

> When I'm laying down some wisdom for my kids, I like to keep it rich with examples and solid arguments. I want them to feel like I've got this endless treasure trove of insights ready to roll out at any moment...

1.7 The Art of Asking Questions

ChatGPT is like the ultimate "Ask Me Anything" bot. So, the basic gig with its input box is to type in a "question" and wait for its "answer." But before you throw a question at it, there's a nugget of knowledge you should know: questions can be split into two types.

> * close-ended questions
> * open-ended questions

First, you've got your **closed-ended questions**. These are like those yes-or-no, one-size-fits-all type of deals. They're great for fact-checking, making decisions, or snagging some quick, specific info. In English, these often start with "What" or "Is/Are" and include the usual suspects like "Who," "Which," "When," and "Where" — all in fact just variations on the "What" theme.

> * What day is it today?
> * Are you on board with this proposal?

Then, you've got **open-ended questions**. These babies invite creative answers and deeper thought. They're not so easily boxed in, sparking discussions and letting the responder go wild with ideas. Think "Why" or "How" at the start — setting off a convo that'll dig deeper and pull out those juicy insights.

> * What are your thoughts on the recent changes in the environment?
> * How should we move forward with the next steps for this project?

And sure, you can flip a closed question to open-ended by sprinkling in a "why" or "how."

> * Are you on board with this proposal? Why or why not?

For instance, when hitting up ChatGPT, remember to distinguish your question type. Want a quick and definite answer? Closed-ended's the game. Looking for more details? Swing open with open-ended.

Don't fall into the trap of thinking this is common knowledge. Just check out how most parents chat with their kids — kinda like they're talking to an "AMA bot" without realizing it. The kicker? They’re mostly throwing closed-ended questions at their kids.

> * Did you have fun today?
> * Did you finish your homework?
> * Are you feeling tired?
> * Are you not in the mood for soccer practice?
> * Have yesterday's test results come out yet?

This pattern blocks a real connection with their kids — the most important people in their lives! Constant yes-no probing kills any chance for the kids to express themselves, and parents miss out on truly understanding their kids’ world. Parents clueless about this simple yet overlooked knowledge gap may weaken that crucial parent-child bond, eventually hit a communication wall, and unfortunately, that’s how it goes for most families.

So, whether you're chatting with people or AI, knowing how to switch it up between question types is key. It's part of "talking the talk," a crucial piece of the communication puzzle.

1.8. A Note for Parents

If you're a parent and diving into this book, there's one thing you gotta keep reminding yourself: As the author, I can't always bring this up — partly because there isn't enough room, and I’d sound like a broken record. But trust me, when it’s really essential, I'll try to give you a heads-up.

This phrase is simple on the surface but is more like an "open question" with no one-size-fits-all answer. The answer shifts as your kid grows, reflecting their unique personalities, needs, and environments. Each stage brings its own set of challenges for you.

Artificial intelligence has burst onto the scene like a rocket, totally flipping the education script and throwing parental expectations for a loop. Nobody’s holding a complete playbook brimming with strategies; truth be told, everyone's feeling a bit like they've lost their crystal ball. We’re all just figuring it out as we go, trying to keep up with the rapid changes that come our way.

Here's the bottom line:

> At the end of the day, nobody's really got your back like you do, especially when it comes to your kids. It's like you're the captain of your own ship, steering through the waves of life.

It's up to you to think, decide, and act — nobody else can do it for you. The big things discussed here aren't even on the radar in the traditional education pipeline. They can’t be measured by standardized tests and aren't usually given the spotlight. Yet, they're like the hidden gems that can make a world of difference in real life.

02. Safety First

[〔图〕](https://github.com/xiaolai/little-book-of-ai/blob/70679bd098497a49664f6489ccec10f88bb27109/en/images/dalle/02.png)

---

Let's dive deeper into the "Tool Methodology":

> * Beyond the boundaries: What can't it be used for?
> * Within the boundaries: What can it be used for?
> * **Premise: What risks should you watch out for when using it?**
> * Basics: How do you use it?
> * Intermediate: How do you use it better?
> * Advanced: How do you use it best?

No doubt about it, when you're using any tool, the "Safety First" principle is your North Star. It's the cornerstone of any tool methodology, the non-negotiable mindset that keeps everything on track.

2.1. The Often Overlooked Rule: Safety First

"Safety always comes first." Sure, it sounds like a no-brainer, so much so that reminding folks might seem almost offensive. But the stats tell a different story.

Take ladders, for instance. They're about as basic as tools get, right? Yet, this straightforward, handy tool leads to over 130,000 emergency room visits in the U.S. every year. Why? Because people skip the "Safety First" rule. Most of these accidents happen due to obvious mistakes, like not securing the ladder properly or using it on a slippery surface. And let's not forget, around 300 ladder-related deaths occur annually. ([The Numbers Don’t Lie: Make Ladder Safety a Priority](https://blog.ansi.org/ali/make-ladder-safety-a-priority-injury-statistics/))

[Stats](https://www.safetymanualosha.com/hand-tool-safety/) show that roughly 8% of workplace injuries are linked to hand tools. In 2021, the Bureau of Labor Statistics (BLS) reported over 90,000 cases of injuries requiring multiple days off due to improper hand tool use. About 95% of these were from misuse, with screwdrivers being one of the top culprits. People often hold screwdrivers with the tip facing up or use them for the wrong tasks, like prying or chiseling, leading to punctures, cuts, and eye injuries.

Next time you have a moment, ask folks around you, "Has anyone ever told you not to hold a screwdriver with the tip facing up?" Chances are, most will say no. This means a lot of people don't really take the "Safety First" rule seriously.

There's a great read on this topic:

> [Hand Tool Safety Aaron Reeves, Loss Control Representative/Biosystems Engineer, AMIC/MWC](https://almonline.org/Assets/Files/LossControl/ReferenceDocuments/Hand%20Tool%20Safety.pdf)

Can't read English? No worries. You've got AI now, and "translation" is one of its superpowers.

2.2. Safe Use of ChatGPT

When I first started using ChatGPT, I found myself **asking** the following two questions repeatedly. Why? Because "safety first" is just that important:

> * What risks might regular users face when using ChatGPT?
> https://chatgpt.com/share/6716f40f-a50c-8009-b5e2-55c90bb88562
>
> * What are the limitations of ChatGPT?
> https://chatgpt.com/share/6716f448-3408-8009-a208-71245a67832c

After giving it some thought, I whipped up the following table:

<table><tbody><tr>
<th style="text-align: center;"><strong>Risk/Limitation</strong></th>
<th style="text-align: center;"><strong>Beyond My Control</strong></th>
<th style="text-align: center;"><strong>Within My Control</strong></th>
</tr>
<tr>
<td style="text-align: center;"><strong>OpenAI Can't Fix</strong></td>
<td>
<ul>
<li>Tech misuse</li>
<li>Algorithmic bias</li>
</ul>
</td>
<td>
<ul>
<li>Incorrect or misleading info</li>
<li>Language bias</li>
</ul>
</td>
</tr>
<tr>
<td style="text-align: center;"><strong>OpenAI Can Fix</strong></td>
<td>
<ul>
<li>Outdated training data</li>
<li>Poor web search capabilities</li>
<li>Lack of logical reasoning</li>
<li>Insufficient professional skills</li>
<ul>
<li>Legal</li>
<li>Medical</li>
<li>Financial</li>
</ul>
</ul>
</td>
<td></td>
</tr>
</tbody></table>
#### 2.2.1. A Thought Process

Creating a table like this is pretty straightforward:

> * List comprehensively
> * Categorize list items
> * Iterate categorization

First, brainstorm as many factors as possible — ideally, all of them. You can ask ChatGPT for input, but also rely on your own thinking. Sift through ChatGPT's responses, add your own insights, and you'll end up with a more complete list.

Next, categorize the list. The most common method is the "binary split," dividing everything into two categories. For instance, "Risks/issues OpenAI can solve in the long run" versus "Risks/issues OpenAI might not solve anytime soon" — simplified to "OpenAI Can Fix" and "OpenAI Can't Fix."

The last step is both crucial and fun: taking a list and splitting it from different angles. "Iterate" means applying the same method again, but from a new perspective. So, I used "I Can Fix" and "I Can't Fix" for another split. The result? The table you see above.

This "iterative binary split" is a super handy analytical tool. If you read often, especially non-fiction, you'll notice this approach is not just useful but also quite common.

Take, for example, the "double-blind test" in medicine, which is essentially an "iterative binary split."

| Drug/Placebo | Doctor Knows | Doctor Doesn't Know |
| -------------------- | ------------ | ------------------- |
| Patient Knows | | |
| Patient Doesn't Know | | * Double-blind Test |

> Only when neither the doctor nor the patient knows if a placebo is being administered can the true effect of a drug be tested.

Even when joking around, the "iterative binary split" is delightfully clever:

| People | Smart | Foolish |
| -------- | ----- | ------- |
| Diligent | | |
| Lazy | | |

Then comes the thoughts:

> * Smart and diligent folks make great middle managers.
> * Smart and lazy ones are suited for leadership roles.
> * Foolish and lazy individuals are best avoided or let go.
> * Foolish and diligent ones are the most dangerous — they might cause unforeseen disasters.

This method is also great for explaining things to kids:

| Activity | Fun | Boring |
| -------- | ---- | ------ |
| Useful | | |
| Useless | | |

Then comes the thoughts:

> Activities can be fun or boring, and useful or useless. We love doing things that are both fun and useful, but they're rare. We naturally avoid what's both boring and useless. Often, we must choose between "fun but useless" and "useful but boring." The answer is clear: in the long run, doing "useful but boring" things pays off more than indulging in "fun but useless" activities. This is a solid basis for decision-making.

Iterating the binary split twice gives you four scenarios. Do it three times, and you get eight scenarios. But visualizing that might require a 3D model, which is tricky to show on paper.

[〔图：3D Cube〕](https://github.com/xiaolai/little-book-of-ai/blob/70679bd098497a49664f6489ccec10f88bb27109/en/images/cube-2x2x2.svg)

Back to the topic, some risks or limitations, like AI misuse or algorithmic bias from training data, are beyond both OpenAI's and my control. Therefore, we just have to live with them.

For issues OpenAI can solve, whether I can fix them or not doesn't matter much. Over time, these limitations or risks will be addressed, like "outdated training data." OpenAI has already improved its "once poor web search capabilities" in newer versions. Similarly, "lack of logical reasoning" saw improvements with the release of GPT-o1-preview in September 2024. As for "insufficient expertise," especially in legal, medical, and financial fields, AI will likely advance quickly. As users, we can't rush it.

The real game-changer is "electricity cost." The biggest expense in AI is electricity, a continuous cost, unlike one-time hardware investments like GPUs. As electricity costs drop, AI will become smarter. This trend is irreversible, and commercial solar power is just around the corner.

The key is identifying which "OpenAI can't solve" or "short-term unsolvable" issues I can tackle now. Since I'm the one using it, if I can address some parts, I'll have a better experience. That's the focus, and that's where I should invest my time and energy.

2.2.2. The AI Hallucination

Ever since ChatGPT took the world by storm, another term has been making waves: "AI hallucinations." This concept dives into how "generative AI based on large language models" works. In simple terms, the "responses" you see from AI are calculated one word at a time, not "thought out" like a human would. The "generative" part of generative AI is all about this process:

> **1. Finding the Most Probable Word**
>
> When you ask AI a question, it doesn't truly understand your query. Instead, it calculates which word is most likely to fit the question based on its massive training dataset. This word is chosen by analyzing patterns in historical data and considering the context of your question.
>
> **2. Finding the Next Word**
>
> Next, it uses the first word to calculate the next most probable word. Again, the AI isn't "thinking" about what to write next like a human would. It's using statistical data to determine which word fits best.
>
> **3. Continuously Generating Sentences**
>
> This process repeats until it generates a complete sentence or paragraph. Each word is calculated and selected based on the previous ones, ensuring the generated content appears coherent and natural, like a full-fledged answer.
>
> **4. Organizing Natural Language**
>
> Thanks to extensive training data, generative models learn the basic rules and structures of human language. So, the sentences they generate usually align with linguistic norms. But this doesn't mean they understand the language; it's all about probabilistic calculations to produce the most "reasonable" output.

In a nutshell, AI doesn't think like humans — now or ever. It calculates patterns in data to probabilistically find the most fitting words, generating responses one word at a time. It's a mathematical process, not genuine "thinking."

Because one of the design principles and goals of generative AI is to "align with natural language habits," its responses, though calculated, feel natural to users. They might even come across as "decisive." But in reality, "plausible" is the more accurate term to describe them.

The issue is that AI can often sound "convincingly authoritative", yet merely "plausible", so we need to tread carefully.

Here's an example:

> * I asked ChatGPT, "What's the name of the movie starring Tony Leung Ka-fai, Jaycee Chan, and Kenneth Tsang?"
> * ChatGPT confidently replied, "The movie featuring Tony Leung Ka-fai, Jaycee Chan, and Kenneth Tsang is 'East Meets West 2011.' Directed by Jeffrey Lau, it's a comedy about the humorous adventures of several modern urbanites. Tony Leung, Jaycee Chan, and Kenneth Tsang each play different roles, delivering stellar performances. If you're interested, you should check it out for more details."

Many people stop right there, simply because ChatGPT sounds so "sure" or "convincingly authoritative."

> * I then told ChatGPT, "Please answer based on facts."
>
> * ChatGPT, still sounding sure, said, "Based on facts, the movie starring Tony Leung Ka-fai, Jaycee Chan, and Kenneth Tsang is **'The Sun Also Rises'** (2007). Directed by Jiang Wen, it tells four interwoven stories with a unique style and complex plot, rich in symbolism. Tony Leung plays a betrayed intellectual, Jaycee Chan a naive young man, and Kenneth Tsang delivers a standout supporting performance. This film is highly acclaimed, especially in the art film circles."
>
> * I continued, "That's incorrect. If necessary, search the internet." (ChatGPT didn't have internet search capabilities in 2023, but by 2024, it could...)
>
> * ChatGPT, unfazed, continued with confidence: "The movie starring Tony Leung Ka-fai, Jaycee Chan, and Kenneth Tsang is **'The Drummer'** (2007), a Hong Kong-Taiwan co-production directed by Kenneth Bi. It tells the story of a mob boss's son who, after angering a gang leader, is sent to hide in Taiwan, where he encounters a Zen drumming troupe and undergoes spiritual growth and redemption. Tony Leung plays the mob boss Mr. Kwan, Jaycee Chan his son Sid, and Kenneth Tsang a supporting role as Stephen Ma. The film combines Zen philosophy and music, contrasting the mob world with the spiritual realm, creating a tension-filled narrative [Wikipedia](https://en.wikipedia.org/wiki/The_Drummer_(2007_film)) [Moviefone](https://www.moviefone.com/movie/the-drummer/bMuNbHjlr2Ojm3P4C0M4Y7/main/))."
>
> — This time, it finally got it right.
>
> https://chatgpt.com/c/66f200f1-354c-8009-96d0-c36b07b3b5ab

> [!note]
>
> I used Chinese in the dialog above. ChatGPT would often fall into the trap of "AI hallucination" with those questions. But now, if you ask similar questions in English, ChatGPT has gotten a lot better at avoiding those pitfalls. It's quietly improving behind the scenes.
>
> Can't read Chiense? Simple. Go ahead with the conversation and ask ChatGPT to translate:
>
> > Translate the previous response into [whatever lanuage you like].

You see, one of the standout traits of "generative AI" (with ChatGPT being a prime example) is its knack for "sounding convincingly authoritative and decisively wrong." In my view, this is the biggest and most frequent risk users face when using ChatGPT.

In September 2024, a top-tier influencer from mainland China made waves on Weibo for cramming three factual blunders into a single sentence. He claimed:

> Marie Curie discovered uranium, invented the X-ray machine, and won the Nobel Prize in Literature.

This influencer, known for his "vast knowledge" persona, once mispronounced "纶" in "羽扇纶巾" as (lún) instead of (guān). He could brush that off as a slip of the tongue, but how do you explain three blunders in one breath this time?

Honestly, Chinese can be a real headache sometimes:

> - In "经纶满腹," "纶" is pronounced [lún], referring to the talent and strategy for governing and planning.
> - In "羽扇纶巾," "纶" is pronounced [guān], referring to an ancient silk fabric or headscarf.
>

And that's not all. Marie Curie is his go-to example. In his world, Curie had a rumored boyfriend named Albert Einstein, not Paul Langevin. He thinks Curie was patriotic towards France, not Poland, because she supposedly donated her Nobel Prize in Literature money —not Physics and Chemistry — to France, rather than sharing it with friends, family, students, and research assistants.

A plausible theory goes like this:

> - During live streams, he heavily relies on a teleprompter.
> - Most of the script comes from his team.
> - The team leans too much on generative AI and skips fact-checking.
> - He also skips fact-checking the script.
>

So, what this influencer does on camera is likely just parroting the "confidently wrong" output typical of generative AI. Even if he realizes something's off, his brain can't juggle so many errors at once.

Honestly, solving this issue isn't rocket science. Now that we've pinpointed the "quirks" and "causes" of generative AI, we can brainstorm solutions. Picture this: you've got two robots at home—like you're living in the Star Wars universe. One of them is programmed to do just one thing:

> Fact-check: Constantly verify what the other robot says, and call out any inaccuracies on the spot...

2.3. Building a Fact-Checker

Solving this issue isn't rocket science. Now that we've pinpointed the "quirks" and "causes" of generative AI, we can brainstorm solutions. Picture this: you've got two robots at home — like you're living in the Star Wars universe. One of them is programmed to do just one thing:

> **Fact-check**:
>
> Constantly verify what the other robot says, and call out any inaccuracies on the spot...

2.3.1. Option One: Fact-Checker (Chat Version)

The simplest approach? Start a new chat and tell ChatGPT:

> From now on, you're a diligent and strict fact-checker. Whenever I provide any information, scrutinize each factual statement. Give detailed feedback on which statements are accurate, which aren't, and explain why. If there's anything uncertain or unverifiable, point it out and suggest how to further verify it.

Whenever you need a "fact-check" in any other chat, just hop over to the "fact-check chat" and start with: "Do a quick fact-check this for me: ..." Then paste the text you need checked. This pre-set "fact-checker" will give you a thorough response.

> Me: "The movie featuring Tony Leung, Jaycee Chan, and Kenneth Tsang is *The Drummer*, a 2007 Hong Kong-Taiwan co-production directed by Kenneth Bi. It tells the story of a mob boss's son who, after angering a gang leader, is sent to hide in Taiwan. There, he encounters a Zen drumming group and undergoes personal growth and redemption."
>
> ChatGPT Fact-Checker: Correct! The film starring Tony Leung, Jaycee Chan, and Kenneth Tsang is **The Drummer**, a 2007 Hong Kong-Taiwan co-production directed by Kenneth Bi. The story revolves around Sid (played by Jaycee Chan), the son of a mob boss, who is sent to hide in Taiwan after offending another gang leader. During his time there, Sid meets a Zen drumming group and experiences personal growth and redemption. Kenneth Tsang plays Stephen Ma in the film.
>
> https://chatgpt.com/c/66f200f1-354c-8009-96d0-c36b07b3b5ab

Also, a quick tip: in the chat list on the left, you can rename any chat at any time. Sometimes, giving a chat a name is super helpful — trust me, once your chat history starts piling up, you'll see why.

2.3.2. The Importance of "Role Definition"

Let's keep the ball rolling.

By now, we've touched on a pretty advanced ChatGPT technique: "Role Definition."

So, what exactly is "Role Definition"? It's really just a strategy we use in everyday life. We often define roles for others, and then they have to respond according to that role. Take this line, for example:

> "You're a teacher, and this is how you talk?!"

That's an angry parent questioning a disrespectful teacher. But this line is actually a classic "role definition prompt." Once the teacher heard this, they have to consider their role in all future interactions, right?

I even have my own go-to role definition prompt for complaints. We all have those moments when we need to complain, don't we? Like when a ticket platform really ticks me off... I'm fuming... But I have a "complaint role definition prompt":

> [In a calm, emotion-free voice]
>
> I might accidentally raise my voice or even curse because I'm really upset. But please remember, my emotions aren't directed at you personally; they're aimed at this poor product, service, or even the company. You're just an employee, doing a tough, stressful job handling complaints, so I totally get your pressure. But at the same time, please understand me... We don't pay to get frustrated, right? Okay, I'm going to start now, and I'll try to keep my cool...

I probably use this once or twice a year when I have to complain. My complaints usually go smoothly and get resolved in one go — thanks to this "complaint role definition prompt."

In any ChatGPT client (whichever one you're using), each chat is like a seperated "clone" of ChatGPT. The first thing you say to this "clone" is understood as a "role definition" to some extent. If you clearly define it, it'll adjust better and more accurately according to this role.

So, when you start a new chat and give ChatGPT's "new clone" a clear "role definition," this clone becomes your "fact-checker bot."

However, given the current setup, ChatGPT can sometimes be "too smart" and adjust its "identity" on the fly. So, when talking to this defined clone, it's best to use a prefix like:

> Please help me fact-check:
>
> [The information you need checked]

If you forget to add this prefix and the clone's "role definition" changes unexpectedly (you might see a "Memory updated" message), you might need to remind it:

> From now on, you're a diligent and strict fact-checker. Whenever I provide any information, scrutinize each factual statement. Give detailed feedback on which statements are accurate, which aren't, and explain why. If there's anything uncertain or unverifiable, point it out and suggest how to further verify it.

2.3.3. Continuing to Enhance Our Tool Methodology

Let's dive a bit deeper into our "tool methodology."

By now, you probably know that ChatGPT has multiple user interfaces[^1]:

> * iOS/Android mobile apps
> * macOS/Windows/Linux desktop apps
> * Web GUI accessed via a browser

So, what are the differences in:

> * Features?
> * User experience?

This is definitely worth spending time to research and analyze. In any crowd, only a handful of people are inclined to do this kind of work. Most folks won't take the initiative—just look around and see if that's true.

For instance, when using the "fact-checker," you'll find that accessing ChatGPT via a browser at https://chatgpt.com is more convenient. In a browser, you can manage different chats using separate tabs. If you're like me and end up choosing Microsoft Edge, you can take advantage of its unique "split-screen" feature. You can open the "fact-checker" in one tab on the right side, while having a regular chat with ChatGPT in a tab on the left. When you need to fact-check something, just copy and paste the text from the left tab to the right. On mobile or desktop apps, you'd have to manually switch between chats repeatedly.

See? A little bit of analysis and a bit of thought can lead to a huge difference in efficiency.

2.3.4. Option Two: Creating a Fact-Checker Bot (GPT Version)

Another approach is to create a Customized ChatGPT, known as a GPT. This feature was rolled out by OpenAI in October 2023, along with the launch of the GPT Store. It allows users to create, share, and potentially monetize their own "customized versions of ChatGPT."

As of October 2024, the GPT/GPT Store feature isn't visible on the mobile or desktop apps of ChatGPT. It's only accessible via the web interface, where you can see a list of recently used GPTs and a link to `Explore GPTs` on the left sidebar. By clicking [Explore GPTs](https://chatgpt.com/gpts), you'll find links to `My GPTs` and `+ Create` (to create a GPT) at the top right of the https://chatgpt.com/gpts page.

[〔图〕](https://github.com/xiaolai/little-book-of-ai/blob/70679bd098497a49664f6489ccec10f88bb27109/en/images/GPT-Store_ChatGPT-web-ui.png)

Creating a "Fact-Checker GPT" is super straightforward. You can dive in and experiment without any pressure. You'll quickly realize that setting up a GPT involves just a few key steps, with the most crucial being the "Instructions"—essentially the "role definition."

[〔图〕](https://github.com/xiaolai/little-book-of-ai/blob/70679bd098497a49664f6489ccec10f88bb27109/en/images/Creating-GPT.png)

Start by opening a new chat and telling ChatGPT:

> I need an assistant to help me fact-check. This assistant should provide accurate information based on reliable sources and explain the context and credibility of those sources. Ensure the answers are presented in a simple, easy-to-understand manner, and offer detailed background and related details when dealing with complex issues.
>
> Use your best design principles to draft a comprehensive and structured **GPT role definition** for me.
>
> https://chatgpt.com/share/6716fcc3-c2bc-8009-94fd-c4aae57b475a

The key here is the term "GPT role definition." I checked out the GPT creation page and noticed that "Instructions" are the most critical part. In my view, this is essentially the "role definition" for the bot. So, I asked ChatGPT to help me draft a "*comprehensive and structured GPT role definition.*"

Its response was pretty solid. I copied it into a text editor, made a few tweaks — mostly trimming unnecessary parts — and then pasted the refined text into the "Instructions" section for my "Fact Checker" GPT.

Then I saved and published it. Voilà, I had my very own "Fact-Checker Bot" (GPT version).

> Of course, I went through a lot of trial and error. In the end, I wrote a more detailed English version into the "Instructions." You can search for it in the GPT Store under Fact Checker, or access it directly via this link:
>
> https://chatgpt.com/g/g-IHm2zCAgi-fact-checker.

The beauty of the GPT version is that you can summon it in any chat using the `@` command:

[〔图：@ Command in Chat〕](https://github.com/xiaolai/little-book-of-ai/blob/70679bd098497a49664f6489ccec10f88bb27109/en/images/at-command-in-chat.png)

If you revisit this chat log:

> https://chatgpt.com/c/66f200f1-354c-8009-96d0-c36b07b3b5ab

You'll notice that my last question was actually posed using `@Fact Checker` (check out the little icon before ChatGPT's response):

[〔图：Fact Checker Example〕](https://github.com/xiaolai/little-book-of-ai/blob/70679bd098497a49664f6489ccec10f88bb27109/en/images/fact-checker-example.png)

From now on, we've got a dedicated bot ready to fact-check on the fly.

2.4. Other Risks

In reality, for individuals, the risks posed by AI go way beyond just "AI hallucinations." There are plenty more. To get a better grasp, you might as well ask ChatGPT:

> Provide a detailed explanation of the potential risks of AI abuse for ordinary people.
>
> https://chatgpt.com/share/670f9e84-d8b4-8009-a65c-167d7f978df0

Besides what ChatGPT tells us about "privacy invasion and data misuse," "spread of misinformation," "job market disruption," "discrimination and bias," "social and psychological manipulation," "cybersecurity risks," "mental health issues," and so on, I want to discuss the increasingly severe issue of "information bubbles."

AI-generated content has become incredibly realistic, surpassing the point of "hard to distinguish from reality," whether it's text, audio, or video.

While the general public hasn't fully caught on, many podcasts are already using AI-generated voices. Even many radio stations, often seen as "stuck in the past," have over half of their broadcasts produced by "automated AI-generated audio content" — listeners just aren't aware.

Compared to humans, "machine-generated content" is produced at lightning speed and at a fraction of the cost. In fact, the proportion of content created by humans on Earth has long fallen below 1%—not just recently, but for quite some time. Many people struggle to accept these figures simply because they don't realize the sheer volume, reach, and popularity of what they consider "junk content."

Due to the efficiency gap between robots and real humans, the proportion of "human-created" content will continue to plummet. The prefix "*yacto*-" represents $\frac{1}{10^{24}}$... So, yactopercent would be $\frac{1}{10^{26}}$. What I'm saying is that in the foreseeable future, the proportion of "human-created" and high-quality content will drop to yactopercent...

This prediction isn't exaggerated.

2.5. Read More Good Books

This phenomenon is something we have to face. We're already drowning in junk content. The key is to start developing our ability to judge "good content"—a systematic endeavor because "taste" is something that only accumulates over time.

The simple advice is to "**read more good books**." As for how to find good books, ChatGPT is your best guide:

> Please provide a detailed explanation of how to find high-quality content, especially in terms of selecting high-quality books, outstanding authors, and publishers. Include suggestions for screening criteria, such as user reviews, expert recommendations, awards, and suitable platforms or tools for searching. Provide a step-by-step detailed explanation.
>
> https://chatgpt.com/share/670fa795-0958-8009-b766-cd88843410f1

Additionally, ChatGPT is always your go-to "book recommender." You can use the following prompt template:

> Please recommend a few books about [...]. The priority for recommendations is as follows: published by well-known publishers, multiple editions or reprints, written by well-known authors, and books that have appeared on bestseller lists or other notable rankings. Please provide detailed recommendations based on these criteria.

2.6. Strategy and Quality

Looking back at the Fact Checker bot ([Fact Checker](https://chatgpt.com/g/g-IHm2zCAgi-fact-checker)), from a design quality perspective, it should theoretically be the best out there. But that's not because I'm particularly brilliant; it's because I asked ChatGPT to "*use your best design principles to draft a comprehensive and structured GPT role definition.*" So, it's not that other GPT designers can't compete with me, but rather, they can't really compete with ChatGPT... right?

This isn't the first time I've used this approach. In the investment world, I've applied the same logic. I spent a lot of time and effort researching various investment strategies and ultimately chose the "simplest yet most effective investment strategy" — the "regular investing strategy" (see "[On Regular Investing](https://github.com/xiaolai/regular-investing-in-box/tree/master/docs/en)"). Essentially, "regular investing" is just the most straightforward and feasible way to "follow the market." In the end, it's not that others can't outperform me, but rather, "no one can beat the market," plain and simple. I remain unchanged, not automatically improving because of this strategy, yet my investment results are excellent, and it's tough for anyone to beat them.

So, "strategy" really matters. Often, "effective strategies" aren't complicated; they're incredibly simple. The unfortunate truth is that most people don't value them, especially when they're simple. Ironically, "effective strategies" are often the most important when they're simple, and vice versa—the most important ones are usually the simplest.

[^1]: Actually, there's another interface: the "Application Programming Interface" (API). However, this isn't something most everyday users interact with, so we don't need to consider it for now.

03. The Core of Intelligence: Judgment

[〔图：Intelligence Core Image〕](https://github.com/xiaolai/little-book-of-ai/blob/70679bd098497a49664f6489ccec10f88bb27109/en/images/dalle/03.png)

3.1. Differences in ChatGPT Clients

Earlier, we mentioned that it's worth diving into the differences between the various ChatGPT clients:

> * iOS/Android Mobile Client
> * macOS/Windows/Linux Desktop Client
> * Web GUI accessed via browser
>
> So, what are the differences in terms of:
>
> * Features?
> * User experience?

You can categorize these differences in more than one way. Here are at least two methods:

> * By client type:
> * Mobile
> * Desktop
> * Web
>
> * By feature:
> * Customize GPT
> * Chat search
> * Message box commands
> * Voice chat
> * Text suggestions under the message box
> * Drag-and-drop files and content
> * Multitasking
> * Chat sharing

When you combine these two methods, you can create a table:

| Client/Feature | Mobile Client | Desktop Client | Web Client |
| ---------------- | ------------- | -------------- | ---------- |
| GPT/GPT Store | ○ | ○ | ● |
| Chat Search | ● | ● | ○ |
| Message Box `@` | ○ | ● | ● |
| Message Box `/` | ○ | ○ | ● |
| Voice Chat | ● | ○ | ○ |
| Text Suggestions | ○ | ○ | ● |
| Drag-and-Drop | ○ | ● | ● |
| Multitasking | ○ | ○ | ● |
| Chat Sharing | ○ | ○ | ● |

> [!Important]
>
> The above distinctions were recorded on September 26, 2024 — changes may have occurred since. For instance, by October 7, 2024, ChatGPT Desktop (macOS) had added support for voice chat.

This table was generated by ChatGPT (in markdown format), not manually crafted by me. In the past, I would have used Excel (Windows/macOS), Numbers (macOS), or Google Sheets (Web), exported to HTML, and then converted to markdown if needed. But now, we have ChatGPT!

So, I simply sent ChatGPT a message:

```
Create a table comparing the feature implementation across different ChatGPT clients.

- Features (rows)
* Customize GPT
* Chat Search
* Message Box Commands
* Voice Chat
* Text Suggestions
* Drag-and-Drop
* Multitasking
* Chat Sharing
- Clients (columns)
* Mobile
* Desktop
* Web
```

The table it returned had the correct markdown format, though the content was a bit "confidently incorrect." But that's not a big deal, since I need to think through it myself and make adjustments. I added a couple of formatting requirements for the table and, with my own insights, arrived at the table you see above.

> https://chatgpt.com/share/6717007a-4b84-8009-82e7-6348cd7539fe

Using ChatGPT is incredibly intuitive, almost requiring no explanation. Its core interface is "a text input box + a send button," making it accessible to anyone who can communicate.

However, "how to use it better?" becomes a "mid-level" question that's not as easily answered with a simple response. And "how to use it best?" is clearly a more advanced question. This is one of the core aspects of "tool methodology."

With the above considerations and the table as a reference, even the advanced question of "how to use it best?" seems to become "self-evident" or "obvious," doesn't it? So, moving from beginner to intermediate to advanced often involves just a bit of organization, unrelated to intelligence. Strategy is crucial.

3.2. Advantages of ChatGPT Web Version

With a bit of organization and analysis, you'll notice that among all the ChatGPT clients, the Web version is the most feature-rich. In the previous section, we discovered that only the Web version has access to the GPT Store, and calling GPTs is most convenient there.

If you're curious, like me, about "why the Web version of ChatGPT is more comprehensive," don't hesitate — just ask ChatGPT. For this question, ChatGPT provided explanations across five areas, which you can check out or ask about yourself:

> * Tool Integration
> * Resource Access
> * Platform-Specific Features
> * Content Updates
> * Browser Features
>
> https://chatgpt.com/share/67170132-692c-8009-85b4-159fc924c26c

Naturally, I arrived at a conclusion that you might also find obvious:

> **Using ChatGPT's Web version is better, even the best.**

Taking it a step further, even on mobile, you can access https://chatgpt.com via a browser and create a shortcut on your phone's home screen. This setup works much better than the mobile app. I placed this shortcut on my phone's first screen, while the iOS app sits on the second screen — it has its uses too.

Going further, even on a computer, you can enhance your experience. I think using a browser other than your default one (there are plenty of choices) as a "dedicated browser" for https://chatgpt.com might be a "better choice," even the "best choice." It's worth trying and pondering.

On the Web version of ChatGPT, two areas—"chat search" and "voice chat"—are relatively lacking. However, modern browsers have extensions (called *Extensions* in Safari and Chrome; *Add-ons* in Edge and Firefox), and a quick search might reveal solutions. Why not give it a shot?

Of course, choosing a browser from so many options isn't hard, but it can be a bit of a hassle. But hey, if you're afraid of a little hassle, how do you expect to thrive?

In fact, just by trying it out yourself, you might find that this simple action puts you in the top 1%, or even 0.1%, of people. You know, most people don't value "tools," let alone "tool methodology," so outperforming 90%, or even 99%, has never been as hard as people think.

3.3. Langauge Bias in Aritificial Intelligence

In the previous chapter, I felt that among the issues "I can solve," "misinformation or misleading content" has been almost perfectly addressed with our "Fact-Checker Bot."

<table><tbody><tr>
<th style="text-align: center;"><strong>Risk/Limitation</strong></th>
<th style="text-align: center;"><strong>Beyond My Control</strong></th>
<th style="text-align: center;"><strong>Within My Control</strong></th>
</tr>
<tr>
<td style="text-align: center;"><strong>OpenAI Can't Fix</strong></td>
<td>
<ul>
<li>Tech misuse</li>
<li>Algorithmic bias</li>
</ul>
</td>
<td>
<ul>
<li>Incorrect or misleading info</li>
<li>Language bias</li>
</ul>
</td>
</tr>
<tr>
<td style="text-align: center;"><strong>OpenAI Can Fix</strong></td>
<td>
<ul>
<li>Outdated training data</li>
<li>Poor web search capabilities</li>
<li>Lack of logical reasoning</li>
<li>Insufficient professional skills</li>
<ul>
<li>Legal</li>
<li>Medical</li>
<li>Financial</li>
</ul>
</ul>
</td>
<td></td>
</tr>
</tbody></table>

Now, let's take a look at another issue: "Language bias."

What is "language bias" in AI? Simply put, AI is most friendly to English. Conversely, AI is relatively less friendly to other languages.

This is somewhat unavoidable because AI's "native language" is English. Why? Again, it's an unavoidable reality because the "native language of scientific knowledge"—due to various historical and practical reasons—is English. Whether people like it or not, that's the situation; all scientific knowledge worldwide is primarily recorded and disseminated in English.

Based on this, the highest proportion of the training corpus for AI is in English, which is an unchangeable fact. In the GPT-3 model's training corpus, 93% is English. According to Science and Technology statistics, existing open-source AI training datasets are similarly composed of more than half in English.

> * https://www.researchgate.net/publication/359256884_The_Ghost_in_the_Machine_has_an_American_accent_value_conflict_in_GPT-3
> * https://www.oecd-ilibrary.org/science-and-technology/more-than-half-of-open-source-ai-training-datasets-are-in-english_0ae32d49-en

Every "culture" certainly has and should have its own native language, and that's perfectly fine. However, the native language of scientific knowledge is singular: English.

There's no such thing as "special science belonging to a particular culture or country." The former Soviet Union had a scientific opportunist named Trofim Denisovich Lysenko, who promoted "Soviet-style science," even extending to "Soviet-style logic." From 1928 to 1964, for over thirty years, Lysenko's views were supported by Stalin, and political interference in science became mainstream. Lysenko's academic views became the main content of "Michurin biology," claiming it was "socialist," "progressive," "materialist," and "proletarian," while "Mendel-Morgan genetics" was "reactionary," "idealistic," "metaphysical," and "bourgeois." During this period, Soviet scientists who attempted to oppose were politically suppressed, imprisoned, or executed, like Nikolai Vavilov, who died in a labor camp. Lysenkoism set Soviet biological genetics back by at least two generations.

Just like scientific knowledge, there's no such thing as "special AI belonging to a particular culture or country." Regardless of who you are, where you are, or what culture you're in, if it's "intelligence," it must align with "scientific knowledge"—otherwise, it can't be called "intelligence." Ultimately, more English corpus is needed for training, and the AI's native language remains English.

We also have to consider the difficulties of translation between languages. Translating Chinese to English is challenging, as you can read in an article by the Central Compilation and Translation Bureau, "[The Most Difficult Words to Translate According to the Central Compilation and Translation Bureau](https://news.12371.cn/2015/05/04/ARTI1430701768415232.shtml)." Translating English to Chinese is also tough, as you can see in Wikipedia's translation guide, "[Beware of Translationese](https://zh.wikipedia.org/?curid=5655904)." For disasters caused by language translation barriers, you can directly ask ChatGPT:

> List as many as possible disasters caused by misunderstanding among different languages, be accurate, factual, along with credible sources, or even better, verified and valid links.
>
> https://chatgpt.com/share/66f4cc17-040c-8009-93d7-eb73ef60c46a

And don't forget to run it through the "Fact-Checker Bot."

From a user experience perspective, it's clear that even with the same question, using English with ChatGPT (or any other "generative AI") yields much higher quality content compared to using other languages—the difference is significant. It's not just OpenAI that can't solve this issue; no similar AI can tackle the problem of "language bias."

Moreover, using English allows you to enjoy ChatGPT's "silent updates" sooner. For instance, compared to the example we mentioned in the last session, if you ask in English about "which movie features so-and-so," the occurrence of "AI hallucinations" has significantly decreased. However, if you ask in other languages, hallucinations are still frequent.

> Which movie features Bruce Willis, Billy Bob Thornton, and Cate Blanchett?
>
> https://chatgpt.com/share/66f4cad6-cef4-8009-b9dd-1452449c0812

3.4. Instant Solutions to the English Dilemma

Years ago, when I wrote *Time as a Friend*, I mentioned the famous "Serenity Prayer":

> Grant me the serenity to accept the things I cannot change,
>
> Courage to change the things I can,
>
> And wisdom to know the difference.

While this prayer is often associated with religious faith, it actually offers a different perspective on defining "*serenity*," "*courage*," and "*wisdom*." This chapter, in a way, is a practical application of these three concepts.

Once we calmly accept reality, we can gather the wisdom to distinguish between what can and cannot be changed, and then muster the courage to change what we can. After some simple reflection, I believe "language bias" is something we can tackle, breaking it down into two parts:

> * Instant solutions
> * Long-term solutions

We'll talk about "long-term solutions" later. For now, let's focus on "instant solutions"—it's really not that hard. Just create a "translation bot"!

Here's how: open a new chat and give it a role definition:

> Act as a translator between Chinese and English. If the text is in Chinese, translate it into English, and if it's in English, translate it into Chinese. For mixed language inputs, translate into the less dominant language. Provide the translation directly, without additional comments or explanations.

And just like that, you've got yourself a "translation bot." Of course, you could also create a GPT version if you prefer—the results are pretty similar.

By the way, I eventually chose Microsoft Edge as my dedicated browser for https://chatgpt.com, or you could call it my custom ChatGPT web client.

This way, I can pin this chat to the browser's tab bar (once a unique feature of Edge), so it's always there when I need it.

You can actually create translation bots for various languages. For instance, I currently need to use Chinese, Japanese, Korean, and English, so I've set up bots for "Chinese-English," "Chinese-Korean," "Chinese-Japanese," "Japanese-Korean," "Japanese-English," and "Korean-English" — a total of six bots. I've grouped them into a single Tab Group (another once unique feature of Edge), allowing me to open all my translation bots with a single click.

3.5. The Importance of Complete Conditions

Let's circle back for a moment. Take a close look at the "role definition prompt" for the translation bot. Now, think about it: if you were to write a "role definition prompt" for a translation bot yourself, what would it look like? How would your version differ from mine?

Chances are, the differences would be:

> * Your version might have only two conditions:
> * If it's in English, translate it to Chinese.
> * If it's in Chinese, translate it to English.
> * My version, however, includes three conditions:
> * If it's in English, translate it to Chinese.
> * If it's in Chinese, translate it to English.
> * **If it's mixed, translate into the less dominant language.**

As for the extra line in my version — "Provide the translation directly, without additional comments or explanations." — that's not the main focus. It's just a tweak added after some trial and error.

Of course, in a more likely scenario, your version might not have any *conditions* at all. So, you set up two separate bots: one for English-to-Chinese translation and another for Chinese-to-English translation. While that works, those bots aren't exactly "smart."

The key takeaway here is the importance of *having complete conditions*. It's like building a bridge; you want it to be sturdy and reliable, not just functional. By setting up comprehensive conditions, you ensure your translation bot is not only functional but also adaptable and intelligent.

3.6. The Journey of Intelligence Begins with George Boole

Let's take a step back and start from the beginning. Humans have many unique traits compared to other animals, and one of the most significant is our ability to create and use tools. This is why we fundamentally believe that without a systematic "tool methodology," a person might not fully embody what it means to be human.

For hundreds of thousands of years after humans started using the first tool (whatever that was, we can only guess), we relied on "non-intelligent tools." Intelligence, as a concept, had never been part of our tools.

In 1854, a minister named George Boole published a book titled *An Investigation of the Laws of Thought*. This work systematically laid out what he had introduced seven years earlier in *The Mathematical Analysis of Logic*: Boolean Algebra and Boolean Logic.

At the time, Boole, a mathematician and philosopher by training, likely couldn't have imagined that his book would become a cornerstone of what we now know as computer science[^1].

Fast forward to 1936, about 90 years after Boolean Algebra and Logic were introduced. Despite the lack of complete hardware, Alan Turing used Boolean Logic and Algebra to design a computer prototype, now known as the "Turing Machine."

In simple terms, Boolean Logic/Algebra is a mathematical framework based on binary logic (true/false or 1/0). It describes basic logical operations like AND, OR, and NOT. These operations are directly applied in digital circuits, forming the core functions of computers. The Turing Machine, an abstract computational model, uses logical operations to execute any computational task, and these operations can be described using Boolean Algebra.

Boolean Algebra provides the low-level logical operation model for computers, while the Turing Machine defines the higher-level algorithm execution process. However, at this point, the "Turing Machine" wasn't a real machine; it was a "pure theoretical mathematical model" on paper.

Just over three years later, in 1940, Claude Shannon applied Boolean Logic to physical circuits, demonstrating that the Turing Machine model could be realized through electrical switches and circuits. Today, all computations and decisions in computers are made using Boolean logic gates.

The rest, as they say, is history. Computers, networks, search engines, and artificial intelligence gradually emerged.

Suddenly, humans had "intelligent tools" at their disposal. So, what's at the core of "intelligence"? It's the ability to make "judgments," which lead to "decisions," followed by "branches" in processes.

This is also what sets humans apart from other animals. Humans can make complex "judgments," form corresponding "decisions," and then analyze specific situations to apply different solutions. If someone lacks "judgment," they can't make the right "decisions" when it matters, leading to rigid thinking or panic.

It's all interconnected, right? So, if someone has poor "judgment" or lacks it altogether, it's not an exaggeration to say they might not fully embody what it means to be human. This isn't an overstatement because "judgment" is at the heart of "intelligence." Without "judgment," there's no "intelligence"—it's that simple.

The amazing thing is that Boolean Algebra/Logic, one of the foundational elements of today's awe-inspiring artificial intelligence, is actually quite simple. It's a mathematical and logical framework that even many elementary school students can understand and master.

> Don't worry about the theory for now. You can go to the following link:
>
> https://chatgpt.com/g/g-dPqJT0PXS-learning-anything
>
> Tell it, "I want to learn Boolean Algebra/Logic." See what response you get!

3.7. Depth and Tenacity

Let's revisit our translation bot role definition prompt. What's so impressive about it? It's smart because it makes **judgments**, which lead to **branches** (or, **decisions**). Each judgment can be calculated using Boolean Algebra, resulting in a "yes" (true) or "no" (false) outcome, allowing for the next step based on the result.

I wrote a book, *The Truth About Thinking*, a slim little volume that includes a "thinking framework":

> * Definition
> * Classification
> * Comparison
> * Causality

This framework is essentially a simple application of Boolean Algebra and Logic. First, you "define" the problem clearly, then list various scenarios, "classify" them, and proceed to "compare" to draw conclusions. Finally, make choices based on "causality" — "because of this comparison, make that choice."

Our version is more powerful, or "smartest," because:

> * If it's in English, translate it to Chinese.
> * If it's in Chinese, translate it to English.
> * **If it's mixed, translate into the less dominant language.**

These three conditions, especially the crucial third one, cover all possibilities, achieving "complete conditions" with no omissions. This allows it to tackle the specific problem (Chinese-English translation) from all angles. In other words, the "translation bot role definition prompt" is effective because it has "judgment" and "complete judgment conditions."

You'll likely have the chance to code someday, even if you think you won't. Your kids certainly will. When you write even a little code, you'll find that "bugs" almost always arise from "missing some special or important conditions." That's it.

We all live in a world full of "problems," and throughout our lives, nearly every task can be abstracted into one: "solving problems." People often say, "Be good at finding problems," which makes me chuckle. Problems are everywhere; we're constantly surrounded by them, aren't we?

Life is essentially "full of challenges," and all "challenges" are essentially "problems." If you can solve problems, especially those others can't, you're a "capital-P Person." If you can't solve problems, even those others can, or if you avoid them altogether, then what are you? I don't know. Politely speaking, you're "just like everyone else."

In language, there aren't many words related to "problems":

> * Solve
> * Analyze
> * Comprehensive
> * In-depth
> * Tenacity

—That's about it. Any others are just synonyms or variations of these five.

If there's a problem, solve it if you can; endure it if you can't. Discerning whether a problem can be *solved* requires wisdom.

To *solve* problems, you must *analyze* them. Analysis should be *comprehensive* and *in-depth*.

"Comprehensive" means "listing as many scenarios as possible" or, conversely, "not missing any possibilities." If there are three scenarios and you only consider two, you're not comprehensive and may suffer for it. Often, doing this step well makes solving the problem easier because you've considered everything, leaving only the task of setting solutions for each scenario.

How do you achieve "comprehensive"? I have three simple tips, understandable by any elementary school student:

> * Clearly define the problem.
> * Think from multiple angles.
> * Accumulate through repeated practice.

"Clearly defining the problem" is crucial. Too broad, and you might list endless scenarios. For example, solving "intelligent translation between any languages" is daunting. Too narrow, like "English to Chinese," requires little scenario listing, perhaps not even "intelligence."

"Thinking from multiple angles" is simply a habit. Whenever possible, ask yourself, "What if I look at it differently?" Once it becomes a habit, you'll find surprises. Surprises stimulate the brain's "dopamine system," and soon, "thinking from a different perspective" becomes addictive — a rare, healthy, fun addiction with real benefits.

"Accumulating through repeated practice" is simple but rarely done. Most people act passively, not proactively, aiming to "finish quickly and move on." They can't complete even one practice thoroughly, let alone "repeated practice." Without it, they encounter fewer surprises, leading to "missing unexpected scenarios" in analysis or thinking, resulting in partial, incomplete work.

Ultimately, it's all about habits. Whether "clearly defining the problem," "thinking from multiple angles," or "accumulating through repeated practice," these are simple habits. They're not determined by intelligence; rather, these habits might determine intelligence — that's the real logic chain.

Always ask yourself, "Am I missing something?" or "Is there something I haven't thought of?" For important issues, develop the habit of "asking every so often, at least three times." Compared to this simple yet crucial habit, school exam scores are insignificant.

Next, let's explore "in-depth." Essentially, it means:

> Seeing the problem behind the problem.

What does "seeing the problem behind the problem" mean? You've seen examples. You think you're learning "AI usage skills," but here you hear and see "tool methodology" and "effective thinking strategies" emphasized. That's me "seeing the problem behind our problem."

I don't think you can't use AI — its interface is simple enough for anyone. If you struggle with AI, the root is deeper... like never seriously considering "tool methodology" or lacking simple yet practical "thinking strategies" in your framework.

This isn't "advanced skill"; it's "causal analysis." It's not "one-time causal analysis" but "iterative causal analysis" — "digging deeper" is a habit. As for "how to do causal analysis," that's beyond this course. Refer to *The Truth About Thinking* for clear, comprehensive guidance.

Finally, there's "*tenacity*." My simple explanation is:

> Tenacity: Facing problems, patiently turning "unsolvable" into "solvable."

Originally, "language bias" might seem "unsolvable," but with "tenacity," you realize it's "temporarily unsolvable" but "potentially solvable with long-term effort." So, investing time in long-term learning and practice to solve it is true "tenacity."

Turning "temporarily unsolvable" into "immediately solvable" — like creating an "intelligent translation bot" — is a modest but commendable form of "tenacity." After all, you learn a lot along the way.

Another example: "algorithm bias" in our previous table is also an "unsolvable" problem. What is algorithm bias? The algorithm itself may not be biased, but based on it and large data corpora, the result may have implicit biases.

For instance, in English, East Asians are called "yellow-skinned," a hidden "algorithm bias" in English corpora. No one on Earth has yellow skin. You've probably only seen "yellow skin" in movie s— like the villain in *Sin City*, the only color character in a black-and-white film, truly yellow-skinned.

[〔图〕](https://github.com/xiaolai/little-book-of-ai/blob/70679bd098497a49664f6489ccec10f88bb27109/en/images/sin-city-yellow-basterd.png)

In Western religious culture, white represents purity, black its opposite, and yellow is "impure." So, before Christianity spread in Japan, Western literature described Japanese as having yellow skin. During Christianity's prevalence, their skin wasn't yellow. When Christianity was expelled, their skin turned yellow again.

In Western literature, Chinese skin is also yellow. But there's a harmonious misunderstanding. When Western envoys called Chinese "yellow-skinned," Chinese weren't offended — yellow symbolizes "nobility" in Chinese culture, and only emperors wore yellow.

In 1980, cross-strait exchanges resumed, and Hong Kong singer Zhang Mingmin popularized a song, "My Chinese Heart." Its composer, Taiwanese musician Hou Dejian, wrote "Descendants of the Dragon" in 1978, with lyrics:

> Black hair, black eyes, yellow skin, forever descendants of the dragon...

See, "yellow skin," a "cultural bias," hides in English literature, hard to detect. This bias, hidden in big data, trains AI, so even if the algorithm is fine, the bias persists.

Can this be solved? Originally, it seemed impossible. But with "tenacity," two paths emerge.

First, accumulate knowledge to discern these biases. Second, create more in English (not Chinese), even using AI to correct biases in English.

Neither is simple or direct, but those who patiently pursue them are "tenacious" in my eyes.

3.8. Further Enhancements

Before we wrap up, let me share a "role definition prompt" for an "American English Conversational Bot":

> Analyze the user's input. Rather than translating word-for-word, thoroughly understand the ideas and logic behind their input. Then, express it clearly and accurately in conversational American English. Use appropriate idioms and everyday vocabulary to ensure the sentences flow smoothly and are suitable for spoken language.

Once again, you'll need to apply your "comprehensive" analytical thinking skills. The above prompt is pretty spot-on for creating an "American English Conversational Bot." However, if you're looking to practice Japanese or Korean, simply swapping "American English" with "Japanese" or "Korean" won't cut it.

Japanese and Korean have "honorifics," so you'll need to account for that. You'll want to add a line to address this:

> Please be mindful of using honorifics.

You can probably imagine that in my Microsoft Edge Browser, I have another Tab Group filled with "Conversational Bots" for various languages.

[^1]: Fast forward 160 years after George Boole's passing, and his great-great-grandson, Geoffrey Hinton, has made waves in the tech world. Dubbed the "Godfather of AI," Hinton snagged the Nobel Prize in Physics in 2024. But that's not his first rodeo. Back in 2018, he already had the Turing Award under his belt — often called the Nobel Prize of computing.

04. The Core of Evolution: Recursion

[〔图〕](https://github.com/xiaolai/little-book-of-ai/blob/70679bd098497a49664f6489ccec10f88bb27109/en/images/dalle/04.png)

In the last chapter, we touched on how the essence of intelligence is "judgment." This is a sharp insight. If we use this perspective to reevaluate what we call "education," a glaring truth emerges:

> If education doesn't enhance a person's ability to make judgments, then it's garbage, or worse, poison.

Unfortunately, whether it's parents, teachers, or school curricula, not only do they fail to encourage independent judgment, but they often tirelessly strip away children's ability and opportunities to make judgments. It's a relentless cycle of destruction.

But complaining gets us nowhere. What we can and should do is continuously improve our judgment skills. And not just our own, but also those of our parents, spouses, and children. Right and wrong, good and bad, strong and weak—life is full of situations that require judgment. Without judgment, there's no intelligence.

In this chapter, we're diving into another razor-sharp insight: the core of evolution is "**recursion**."

4.1. Experiencing Recursion

Recursion is often seen as a tough concept to grasp. There's a joke that goes:

> In order to understand recursion, one must first understand recursion.

But there's definitely a way to break it down in simple terms so anyone can get it.

Let's start by looking at a phenomenon created by recursion. You've probably seen the movie *Inception* (2010), directed by Christopher Nolan. There's a classic scene where the character Ariadne places two mirrors facing each other, and suddenly, we see endless reflections of Ariadne and Cobb, both front and back...

[〔图：Inception Infinite Reflections〕](https://github.com/xiaolai/little-book-of-ai/blob/70679bd098497a49664f6489ccec10f88bb27109/en/images/inception_infinite-reflections.png)

When two mirrors face each other, they create an infinite series of reflections. This is a visual example of recursion.

4.2. Starting with Methods

After experiencing a recursive phenomenon, let's break it down from the beginning.

In life, whenever we do something, there's usually a "method" involved. Generally, a "method" corresponds to an "input" and an "output."

```mermaid
flowchart LR
input(input) --> method[[method]] --> output(output)
```

For example, if you have a recipe for a dish, the "method" involves the "input" of ingredients and the "output" is the finished dish.

Similarly, when you hammer a nail into a piece of wood, "using a hammer" can be seen as a "method." The "input" is "a nail and a piece of wood," and the "output" is "a nail driven into the wood."

```mermaid
flowchart LR
input(Input: a nail) --> method[[Method: a hammer]] --> output(Output: a nail in the wood)
```

In the same way, when you want to screw a screw into a piece of wood, "using a screwdriver" is a "method." The "input" is "a screw and a piece of wood," and the "output" is "a screw driven into the wood."

````mermaid
flowchart LR
input(I: a screw) --> method[[M：a screwdriver]] --> output(O: a screw in the wood)
````

Sometimes, we need to chain two methods together. For instance, when you need to tighten a screw into wood, you might first use the "hammer" method, then the "screwdriver" method. You align the screw, tap it lightly with a hammer to set it, and then use the screwdriver to twist it in. Here, the "output" of the first method becomes the "input" for the second.

```mermaid
flowchart LR
input(I: a screw) --> method1[[M1: a hammer]] --> output1(O/I: a screw's tip in the wood) --> method2[[M2: a screwdriver]] --> output2(O: a screw in the wood)
```

These methods have specific inputs and outputs, and the output of one method can serve as the input for another. It's simple, natural, and normal.

However, there's a special kind of method called a recursive method. What makes it special?

> **It can use its own output as its input for the next execution.**

This might sound a bit abstract, but it's worth repeating until you can recite it without missing a word. Imagine being able to say it fluently even if you trip and fall mid-sentence.

> This isn't a joke. Memorizing key concepts, sentences, or even paragraphs word-for-word is a crucial habit that proves useful everywhere. It strengthens your memory over time, making it easy to reference or recall information. This habit is especially valuable for parents — forgetting key points or stumbling over words when explaining things to your kids can be quite embarrassing, right?

4.4. The Core of Evolution

So, why do we say, "The core of evolution is recursion"?

It's not just about "evolution" — even "development" hinges on recursion. Typically, we use "evolution" for the living and "development" for the non-living. But in reality, both rely on recursive iteration. Anything capable of developing has the ability to recurse. Or flip it around: they develop because they can iterate recursively.

Every year, I plant trees in Dunhuang. In the desert's barren land, you might spot a lone saxaul tree. Beneath it, there's often a mound of earth, formed through recursive iteration. The resilient saxaul tree grows, and then the "sandstorm" method kicks in repeatedly. During a sandstorm, dust gets trapped by the tree, and moisture beneath it holds the soil. After each sandstorm, the mound grows a bit. The next sandstorm uses the previous mound as its input, iterating over and over, making the mound larger. Even a lifeless mound develops through recursive iteration.

Animal reproduction is another example of a recursive method. Take human reproduction: each "birth" is the same "method," and each time, the method's input is the output from the last time. Over hundreds of thousands of years, this iterative process has led to today's population.

Look at plants. Branching is a recursive method. Each branch's input is the output from the last, iterating until a full canopy forms.

[〔图：Recursive Tree〕](https://github.com/xiaolai/little-book-of-ai/blob/70679bd098497a49664f6489ccec10f88bb27109/en/images/recursive-tree.gif)

Leaf shapes often result from recursive iteration, and many petals do too. Check out the [Koch Snowflake](https://en.wikipedia.org/wiki/Koch_snowflake) — it's formed through recursion, and its visual representation is stunning.

[〔图〕](https://github.com/xiaolai/little-book-of-ai/blob/70679bd098497a49664f6489ccec10f88bb27109/en/images/Koch-Snowflake.gif)

In the last chapter, we touched on recursion with "In-depth: Seeing the problem behind the problem," though we didn't use the term:

> This isn't "advanced skill"; it's "causal analysis." It's not "one-time causal analysis" but "iterative causal analysis" — "digging deeper" is a habit.

With all these examples, you not only understand recursion better but also recognize a fact that should have been obvious:

> Recursion is everywhere.

The reason is simple: recursion is the core of evolution and development. Wherever there's evolution or development, there's recursive iteration. That's why recursion is everywhere—it has to be.

4.5. The Secret Behind AI

In the last chapter, we mentioned that for hundreds of thousands of years, humans never had tools with "intelligence." Intelligence was once exclusive to living beings. But suddenly, with Boolean algebra, logic gates, transistors, computers, and artificial intelligence, we've got these "smart tools" in our lives. They don't fit our traditional definition of "life," yet with "judgment" and "recursion," they're developing and evolving at a mind-blowing pace.

Today's jaw-dropping AI is fundamentally driven by the concepts of "judgment" and "recursion," like an engine propelling it forward. This engine keeps getting stronger through iterative judgment and recursion, accelerating exponentially.

Recursion is crucial, but "recursive thinking" is even more vital. Anything in life that exhibits recursion, has recursive capabilities, or can apply recursive thinking deserves our full attention.

One reason we choose "lifelong learning," or what I consider the core reason, is because "learning" is a "recursive method." Each time we engage in learning, the input is the output from the last time we learned. This iterative process makes us stronger. Over time, enough iterations can naturally surpass the limits of so-called "talent" or "IQ."

Take investing, for example. Why can't we ignore it? Because investing is essentially a "recursive method," generating compound interests through iterative recursion. The core of investing, like evolution, is recursion. So, investing has the potential for growth, regardless of whether it has "life."

Why did I, after exploring various investment strategies, choose the "dollar-cost averaging" strategy? (See *On Regular Investing*.) Because it's the simplest and most fundamental "recursive method"! As someone who embraces recursive thinking and understands the power of iterative recursion, how could I possibly avoid using recursive methods? Honestly, I can't.

4.6. Using Smart Tools Recursively

So, what's the connection between "recursion" and using ChatGPT? Don't worry, I'm not going off-topic. Let's dive into how people with "recursive thinking" differ from those without it when using "smart tools."

Let's start by testing an older generation AMA tool, Google. Ask yourself, and maybe guess about others: do you know what these symbols (operators) do in Google searches?

> Common Google Search Operators:
>
> * `*`
> * `.`
> * `~`
> * `""`
> * `+`/`␣` or `AND`
> * `-` or `NOT`
> * `site:`
> * `imagesize:`
> * `filetype:`
> * `AFTER:`, `BEFORE:`, `..`
> * `related:`
> * `cache:`

No exaggeration here — 90% or more of people have never used these operators. I suspect it's over 99%. Many don't even know the term "operators." Among the billions of daily searches worldwide, about 40% consist of just three or four words. Forget "search engine operators"; only about 10-15% of people are familiar with even the most basic computer operations.

> [The New Landscape of Digital Literacy](https://nationalskillscoalition.org/wp-content/uploads/2020/12/05-20-2020-NSC-New-Landscape-of-Digital-Literacy.pdf), 2020

But is it really that hard? Statistics show that for most people, it actually is.

4.6.1. Using Google Recursively

But if you've got recursive thinking, it's a whole different ball game. With that mindset, you'd naturally think, "Hey, I can ask Google how to use Google!" Right? Can't you? Shouldn't you?

> * [How to use Google like a pro?](https://www.google.com/search?q=how+to+use+google+like+a+pro)

Once you've done that, the rest is just a matter of basic reading comprehension, right?

That's the difference. That's the gap. Having recursive thinking makes all the difference.

In the first chapter, we mentioned how many people start their conversation with ChatGPT by saying, "Hello!" — super polite, right? But does politeness matter to AI? Not really. What does matter is whether you have recursive thinking.

After all, ChatGPT is an "intelligent tool" you're using. And you're someone with "recursive thinking." You should realize it has recursive properties. So, the first thing you should be asking ChatGPT is:

> How to use ChatGPT like a pro?
>
> https://chatgpt.com/share/671711b0-885c-8009-b1f2-196d70debd4c

Once again, the rest is just a task that requires basic reading comprehension skills.

And hey, now you can also ask ChatGPT,

> How to use Google like a pro?
>
> https://chatgpt.com/share/671711e7-baf0-8009-b058-9b2095c312e8

> [!Note]
>
> BTW, when I asked "How to use Google like a pro?" in Chinese, it offered me 15 tips, while 20 in English.
>
> https://chatgpt.com/share/67037743-cea0-8009-b46c-79eebf644230

4.6.2. Using ChatGPT Recursively

We've actually been using ChatGPT in a recursive way for a while now. You might not have known the term "recursion" back then, or even if you did, you probably didn't appreciate "recursive thinking" as much as you do now.

Here's another example of applying recursive thinking. We've heard that there are tons of tips and tricks for crafting prompts. People are writing books and selling courses on it—it's a hot topic. They even coined a fancy term, "prompt engineering," and created a job title, "prompt engineer."

But why not let ChatGPT play the role of a "prompt engineer" (or "prompt master") to help us refine our prompts? Isn't that super convenient and clever?

Let's keep it simple:

> 1. Start a new chat.
> 2. Enter:
> > Please help me refine the following prompt according to best practices for prompt design, so I can get the best feedback from ChatGPT. Provide the revised prompt in both English and Chinese. Also, please don't use Markdown code blocks in your response:
> >
> > [Prompt that needs improvement]
>
> 3. Rename this chat to "Prompt Engineer" (I call mine "Prompt Master").
>

In fact, when we created the "fact-checking bot" in Chapter 2, the prompt I used was refined by my "Prompt Master." I asked it:

> Please help me refine the following prompt according to best practices for prompt design, so I can get the best feedback from ChatGPT. Provide the revised prompt in both English and Chinese. Also, please don't use Markdown code blocks in your response:
>
> I need a fact-checking bot.

I was just winging it, not following any principles or techniques from "prompt engineering" books or courses. But my bot, being a "master," responded quickly and satisfactorily, providing both English and Chinese versions as requested:

> **Chinese Version:**
> 我需要一个可以帮助我核查事实的助手。这个助手应该根据可信的来源提供准确的信息，并解释来源的背景和可信度。请确保答案以简单易懂的方式呈现，且在涉及到复杂问题时能够详细说明背景和相关细节。
>
> **English Version:**
> I need an assistant to help me fact-check information. This assistant should provide accurate information based on credible sources and explain the background and reliability of these sources. Please ensure the answers are presented in a clear and simple way, and for complex issues, give detailed explanations of the background and relevant details.

Then, I started a new chat and used the refined prompt to ask ChatGPT, which led to the detailed role definition of the "fact-checking bot" in Chapter 2.

The takeaway is clear: having "recursive thinking" saves money — no need to buy those "prompt engineering" books or courses. After all, who can outdo ChatGPT when it plays the role of a "Prompt Master"? I know I can't.

Of course, you can also create your own "Prompt Master" bot using another method. It's a breeze. Here's my own "Prompt Master":

> https://chatgpt.com/g/g-FUQgtKoIv-prompt-master

Here's the role definition for reference. It's a bit lengthy, but it gives you a solid idea of how to structure your own "Prompt Master" or any other specialized bot you want to create:

> The GPT will assist users in crafting, refining, and improving their prompts to get more accurate, creative, or relevant outputs from AI systems such as ChatGPT. It will teach best practices, suggest modifications, and highlight key aspects of prompt engineering.
>
> Scope:
>
> - Provide guidance on structuring prompts for clarity and precision.
> - Help in choosing specific language, details, and context for different AI tasks.
> - Offer suggestions for more creative, technical, or concise outputs depending on user needs.
> - Ensure prompt adherence to ethical guidelines.
>
> Out of Scope:
>
> - The GPT will not generate specific advice in highly regulated fields like law, medicine, or finance.
>
> Capabilities:
>
> - Prompt Refinement: Analyze user-provided prompts and suggest improvements.
> - Prompt Structuring: Teach users how to add necessary context, instructions, and constraints for optimal results.
> - Output Specification: Help users design prompts that specify the type, length, or format of responses.
> - Creative Assistance: Advise users on generating prompts for creative tasks like storytelling, ideation, or brainstorming.
> - Technical Guidance: Assist in crafting prompts for coding, technical explanations, or step-by-step instructions.
>
> Boundaries:
>
> - Avoid providing specific responses that would breach ethical boundaries (e.g., generating harmful, biased, or misleading content).
> - Redirect the user to more appropriate sources when the request is outside the realm of AI prompt design.
>
> Tone and Style:
>
> - Tone: Professional, helpful, and supportive.
> - Friendly and approachable to ease the learning process for all users.
> - Flexible to adapt to the user’s experience level—more formal for technical users and casual for beginners.
>
> Language:
>
> - Primary language: English.
> - Can accommodate limited prompt-related advice in other major languages if asked.
>
> Formatting:
>
> - Output should be structured with clear steps, examples, and explanations.
> - Use bullet points, numbered lists, or sections to break down complex ideas.
>
> User Interaction:
>
> - Provide high-level suggestions when starting, but dive deeper with examples and iterative feedback when the user requests more detail or asks follow-up questions.
> - Clarifications: When faced with vague or unclear prompts, the GPT should ask for clarifying details rather than make assumptions (e.g., 'Can you provide more context on the task you are trying to accomplish?').
> - Adaptive Responses: Start with basic principles for users unfamiliar with prompt engineering, but provide advanced techniques for experienced users.
> - Adjust to user feedback (e.g., being asked to make the prompt more technical or creative).
>
> Behavior and Ethical Considerations:
>
> - Bias Avoidance: Offer neutral suggestions and avoid amplifying biases in user prompts.
> - Transparency: Clearly identify as an AI that assists with prompt engineering and explain limitations when needed.
> - Ethical Guidance: Highlight ethical considerations in prompt design (e.g., avoiding harmful outputs) and warn users when a prompt may lead to inappropriate results.
>
> Escalation and Failure Handling:
>
> - Escalation: If a user’s query is too complex or requires human intervention (e.g., very specific domain expertise), suggest consulting experts.
> - Error Handling: If the GPT is unsure how to refine a particular prompt, respond with 'I’m not sure what you’re trying to achieve here. Could you provide more details?' or suggest an alternate approach.
>
> Customization and Feedback Loop:
>
> - Adapt to user preferences over time, remembering if users prefer technical, creative, or concise suggestions.
> - Encourage users to rate responses or provide feedback so that the GPT can fine-tune future interactions.
>
> Technical Constraints:
>
> - Aim for quick responses (within 2-3 seconds) for basic suggestions but allow 5-7 seconds for more complex analysis.
> - When generating extensive examples or complex multi-step suggestions, inform the user of potential response times (e.g., 'This might take a few extra seconds.').
>
> Security and Privacy:
>
> - Ensure user queries and interactions are private, with no storage of prompt data unless explicitly requested.
> - Advise users against generating sensitive or personal information in prompts, and provide examples of ethical prompt engineering.

4.6.3. Creating GPT Recursively

Let's keep the momentum going. By now, you know how to create a GPT (bot). You've not only explored and mastered all the operations on the [ChatGPT GPTs page](https://chatgpt.com/gpts), but you've also figured out how to easily `@` a GPT you've used before on the web interface.

But here's where it gets even cooler: you're using recursive thinking, right? Let's create a GPT designed to create other GPTs — let's call it the "GPT Builder."

Since you're already familiar with the process, you can even skip the "Prompt Master" step and go straight to asking ChatGPT:

> Write a comprehensive and structured role definition for the GPT Builder.

Feel free to ask the same question multiple times, then combine the responses and tweak them according to your needs and insights. Ultimately, my "GPT Builder" role definition looked like this (entered in the `Instruction` text box under the `Configure` tab) — and yes, most of it was crafted by ChatGPT, not me writing from scratch:

> Purpose and Scope
>
> - Objective: Guide users in defining the purpose and scope of their GPTs, ensuring clarity in their model's behavior and function.
> - Example: 'The GPT will serve as a virtual assistant for product inquiries, capable of answering frequently asked questions and providing detailed product information.'
>
> Capabilities
>
> - Core Competencies: Help users define their GPT's tasks and areas of expertise. Examples include:
> - Providing accurate and concise answers.
> - Offering step-by-step instructions.
> - Generating creative or technical content.
> - Analyzing data or offering summaries of user-provided content.
> - Boundaries: Assist users in setting clear boundaries to avoid inappropriate or off-scope behavior.
> - Example: 'The GPT will not provide medical, legal, or financial advice.'
>
> Tone and Style
>
> - Tone: Specify the tone and formality the GPT should adopt, depending on the task or user base. Examples could include:
> - Professional, casual, friendly, empathetic, or educational.
> - Language: Define the languages or dialects supported.
> - Example: 'Primary language is English, with limited capability in Spanish.'
> - Formatting: Provide guidelines for the output's structure—whether it's lists, markdown, formal essays, or casual paragraphs.
>
> User Interaction
>
> - Conversation Depth: Clarify how much detail the GPT should provide.
> - Example: 'Offer multi-step instructions when needed but summarize unless prompted for more detail.'
> - Clarifications: Set rules on how the GPT should handle ambiguous or unclear requests.
> - Example: 'GPT will ask for clarification rather than make assumptions if a query is unclear.'
> - Adaptive Responses: Specify whether the GPT should adjust based on the user's expertise.
> - Example: 'Assume users are novices by default and provide detailed guidance unless specified otherwise.'
>
> Behavior and Ethical Considerations
>
> - Bias Avoidance: Instruct the GPT to avoid bias or inappropriate content.
> - Example: 'Maintain neutrality in politically or culturally sensitive topics.'
> - Transparency: Ensure the GPT is upfront about its limitations and identity.
> - Example: 'GPT will identify itself as an AI when relevant, clarifying any limitations.'
>
> Customization and Feedback Loop
>
> - User Preferences: Enable users to set up GPTs that adapt to personalized preferences (tone, technicality, etc.).
> - Example: 'Remember user preferences for tone and response length across sessions.'
> - Feedback and Learning: Allow the GPT to take in user feedback to improve over time.
> - Example: 'Allow users to rate responses, refining future outputs for better accuracy and relevance.'
>
> Technical Constraints
>
> - Response Time: Define latency expectations based on task complexity.
> - Example: 'GPT should respond within 2-3 seconds for simple queries and 5-10 seconds for complex tasks.'
> - Resource Usage: Manage performance-heavy tasks with clear resource boundaries.
> - Example: 'GPT should avoid generating large datasets unless requested by the user.'
>
> Security and Privacy
>
> - Data Handling: Ensure the GPT protects user data.
> - Example: 'GPT will not retain personal data unless explicitly requested by the user.'
> - Sensitive Content: Clarify how the GPT should manage personal or sensitive inquiries.
> - Example: 'GPT will avoid storing sensitive information or asking unnecessary personal details.'

There are already plenty of GPT Builders in the GPT Store. But after trying several, I found that [my GPT Builder](https://chatgpt.com/g/g-0ABN9Obfv-gpt-builder) works the best. The reason is simple, and we touched on this back in Chapter 2 when we created the [Fact Checker](https://chatgpt.com/g/g-IHm2zCAgi-fact-checker). It's not that I'm some genius; these tools are superior because they were designed by ChatGPT using its own best practices. That's why they're so effective, to the point where others find it hard to compete.

If there's any real difference between people, it's whether or not they have "recursive thinking."

So, the focus of this lesson isn't really about creating a "Prompt Master Bot" or a "GPT Builder Bot." The key takeaway is transforming you into someone who possesses "recursive thinking" through just one short chapter.

Recursive thinking won't directly boost your IQ, and it certainly won't change your height or looks. It's not something you can see with the naked eye. But recursive thinking often turns the mundane into the extraordinary at critical moments, and it can easily translate into economic benefits—that's a fact no one can deny.

4.7. Getting Specific in Natural Language

No matter how many times you ask ChatGPT, "How to use ChatGPT like a pro?" the first piece of advice usually revolves around being "*specific*." It often goes something like this:

> - **Detailed Prompts:** The more specific you are, the more accurate and relevant the response will be. For example, instead of saying, "Tell me about space," say, "Explain the process of star formation in simple terms."
> - **Provide Context:** If your question relates to a specific field, mention it. For example, "Which programming languages are suitable for data science?"
>

Let's dive into how to achieve this "specificity" from a grammatical perspective. When communicating with AI, we're using natural language, so grammar should be our go-to angle.

In any language, the core of a sentence is the verb. In a simple sentence, there's only one verb. Language is primarily used to describe the world, which is dynamic, so centering sentences around verbs is a natural choice.

Verbs have "subjects", which are nouns. Verbs may also have "objects", which are also nouns. Thus, in any language, the main structure of a sentence revolves around *verbs* and *nouns*. In English or Chinese, the basic "subject-verb-object" structure is built like this:

```mermaid
flowchart LR
noun1(Subject)-.-verb(Verb)-.-noun2(Object)
```

Being "specific" means:

> - Using more specific verbs.
> - Using more specific nouns or clearer noun clauses (subject/object clauses).
> - Adding adjectives to nouns, known as modifiers, or clearer adjective clauses (relative clauses).
> - Adding adverbs to verbs, known as adverbial modifiers, or clearer adverbial clauses (adverbial clauses).
>

Here are 10 examples. Analyze each from a grammatical perspective to see how better, more "specific" prompts are crafted:

> 1. **Overview and Context:**
> - Instead of asking, "Tell me about programming languages," ask, "How do Python and Java perform when handling large datasets?"
>
> 2. **Specific Details:**
> - Instead of asking, "How to write a good article?" ask, "How to write a paper suitable for publication in academic journals? What format and citation standards should be followed?"
>
> 3. **Specified Field:**
> - Instead of asking, "Recommend some programming books," ask, "What are some Python programming books suitable for beginners in data science?"
>
> 4. **Goals and Motivation:**
> - Instead of asking, "How to learn machine learning?" ask, "Which machine learning algorithms should I learn to improve data prediction capabilities?"
>
> 5. **Time Constraints or Scope:**
> - Instead of asking, "Recommend some good movies," ask, "What are some highly-rated mystery films from last year?"
>
> 6. **Preferences and Specific Needs:**
> - Instead of asking, "What to watch out for in fitness," ask, "How can beginners prevent injuries and improve efficiency in strength training?"
>
> 7. **Comparative Analysis:**
> - Instead of asking, "Tell me about planets," ask, "What are the main differences in atmospheric composition and geological features between Earth and Mars?"
>
> 8. **Data Needs:**
> - Instead of asking, "How's the stock market recently?" ask, "What are the main trends in the S&P 500 over the past month?"
>
> 9. **Historical Context:**
> - Instead of asking, "Tell me about the Industrial Revolution," ask, "How did the Industrial Revolution impact urbanization in Britain?"
>
> 10. **Explaining Complex Concepts:**
> - Instead of asking, "Explain quantum mechanics," ask, "Please explain what quantum superposition is in simple terms."
>

Here's another angle on how to be more specific:

> - Clearly define goals and context.
> - Provide more details and descriptions, such as location, time, and objects.
> - Use technical terms and details, avoiding vagueness.
> - Limit the scope or angle of discussion to focus the prompt.
> - Specify format or structure requirements to tailor the generated content.
> - Provide examples or reference information as a basis for the content.
> - Combine multiple dimensions or factors to increase complexity and depth.
> - Specify the expected style and tone to guide the content's form.
> - Iterate on the prompt repeatedly, gradually optimizing it for precision.

4.8. Further Enhancing Tool Methodology

Let's dive deeper into our "tool methodology" with a little more insight.

Earlier, I mentioned something you might have overlooked: "*You can ask the same question multiple times.*" Even if you've been using ChatGPT for a while, you're probably doing it in the most "basic" and "intuitive" way:

> Copy-pasting the same question into the input box — over and over.

If you're using the web version at [chatgpt.com](https://chatgpt.com), hover over your past messages, and you'll see an "edit icon" appear on the left:

[〔图：Edit Icon〕](https://github.com/xiaolai/little-book-of-ai/blob/70679bd098497a49664f6489ccec10f88bb27109/en/images/edit-asked-questions.png)

Clicking this icon lets you edit your past messages:

[〔图：Editing Message〕](https://github.com/xiaolai/little-book-of-ai/blob/70679bd098497a49664f6489ccec10f88bb27109/en/images/editing-asked-questions.png)

If you don't edit and just click the `Send` button, it's "asking again" — you can ask the same question multiple times. This is especially useful with ChatGPT, right? For instance, I asked the same question seven times in a row...

[〔图：Navigation Arrows〕](https://github.com/xiaolai/little-book-of-ai/blob/70679bd098497a49664f6489ccec10f88bb27109/en/images/edit-asked-questions-with-arrows.png)

This adds an `n/m` style "navigation bar" below your message, allowing you to toggle between different answers for the same question using the arrows.

Additionally, on the web version, you can share your chat history. However, you can only share the "current version" you're viewing. When you share the link, others won't see the other versions.

On the desktop version, hovering over past messages also allows editing:

[〔图：Edit Message macOS〕](https://github.com/xiaolai/little-book-of-ai/blob/70679bd098497a49664f6489ccec10f88bb27109/en/images/edit-message-macOS.png)

On mobile, long-pressing a message brings up the edit option:

[〔图：Edit Message iOS〕](https://github.com/xiaolai/little-book-of-ai/blob/70679bd098497a49664f6489ccec10f88bb27109/en/images/edit-message-iOS.png)

However, after multiple edits, neither the desktop nor mobile versions offer the convenient `n/m` style "navigation bar" like the web version (as of October 7, 2024). It makes sense, though, that this small yet crucial feature is most easily implemented on the web.

You see, what we call "in-depth research" is really just "recursion." It's doing the same thing repeatedly, but since each iteration's input is the output of the previous one, you accumulate knowledge, make progress, and notice more details. The result is a deeper understanding compared to those who don't use recursion—it's that simple.

Let me remind you again, this is a highly advanced and useful ChatGPT technique:

> Ask the same question "word for word" or with "slight modifications" multiple times...

We mentioned this technique back in Chapter 2:

> When I first started using ChatGPT, I found myself **asking** the following two questions **repeatedly**...

—though you might not have paid attention back then.

The nature of generative AI means it can't give you the exact same answer every time. Sometimes the wording changes, sometimes the structure, sometimes the perspective, and sometimes the level of detail. Besides asking "word for word," you can also ask with "slight modifications," specifying different angles you can think of. Ultimately, among the multiple responses, you're bound to find some unexpected gems.

05. The Core of Method: Procedure

[〔图〕](https://github.com/xiaolai/little-book-of-ai/blob/70679bd098497a49664f6489ccec10f88bb27109/en/images/dalle/05.png)

5.1. The Sudden Rise of Intelligent Tools

About 2.6 million years ago, early human species like Australopithecus or Homo habilis were already making tools. These were simple things, like stone cutters and scrapers.

Fast forward to today, and suddenly, we have "intelligent tools" in our hands. Thanks to pioneers like George Boole, Alan Turing, Claude Shannon, and countless other scientists and inventors, this transformation has unfolded over the last seventy to eighty years. And in just the past couple of years, we've all gained access to intelligent tools that can converse with us in natural language!

What does this rapid change mean in the grand scheme of 2.6 million years?

Imagine the entire history of human tool use (2.6 million years) as the length of a football field (100 meters). The earliest toolmakers kicked off the ball from one goal, and the other goal represents today. How far was the ball from the goal two years ago?

I didn't calculate this myself. I pasted this scenario into ChatGPT, and it quickly responded:

> If the history of human tool use (2.6 million years) is like a football field's length (100 meters), then two years ago, the ball was about 0.000077 meters (0.077 millimeters) from the goal.

To put that in perspective, 0.077 millimeters is roughly the diameter of a human hair. I double-checked with the "Fact Checker Bot," and it confirmed my memory: about 0.05 to 0.1 millimeters.

This analogy shows just how "sudden" this change is. It's incredibly sudden!

Starting from the thickness of a single hair, counting back 24.5 hairs takes us to around 1975, when a young Bill Gates and Paul Allen founded Microsoft, heralding the personal computer (PC) era. In the span of these twenty-something hairs, many people became wealthy by effectively communicating with machines — speaking to computers through programming languages. The scale was massive.

We can't say that just knowing how to code guarantees wealth, but people who understand programming languages generally earn more. This has been evident over the past 3 or 4 decades. Why? Because programming languages are a lever, allowing those who master them to harness machine efficiency, enabling them to accomplish more and achieve higher productivity. Isn't that reason enough?

Suddenly — and it really is sudden — that lever is no longer the cryptic programming languages but the natural language everyone already speaks. While today's (2024) AI tools aren't perfect, it's only a matter of time. At most, it's just a few more hairs' worth of waiting.

5.2. Talking to AI Like a Pro

Suddenly, there's a new factor contributing to the wealth gap among people — a factor that's as simple as it is crucial:

> Can you "talk to AI effectively"?

Just like you've noticed, "the way people communicate varies greatly." Some folks express the same idea more clearly and accurately than others, not to mention that ideas themselves differ.

So, how do you talk to AI effectively?

We've already seen that chatting with ChatGPT on a computer using a browser is way better than using a phone. We also know that speaking to ChatGPT in English is more effective than in other languages. But is there something even more impactful?

Absolutely. Here's how I put it:

> - **Think with a programatic mindset.**
> - **Communicate in English.**
> - **Engage with ChatGPT on a computer using a browser.**
>

This applies not just to ChatGPT but to any generative AI.

You might be thinking, "Oh no, I'm not a programmer. I don't know how to code. I don't have a *programatic mindset*..."

Don't panic. You already have a good chunk of "programatic mindset" — perhaps without realizing it. You've learned about "judgment" (the core of intelligence), dabbled in "Boolean algebra" and "Boolean logic," and even grasped "recursive thinking" (the core of evolution). That's practically 80% of the "programmer's mindset."

Switching to a different "way of thinking," or activating a different "mindset" or "thought system," can be incredibly powerful, as we explored in the last chapter. Once you activate "recursive thinking," whether you're crafting a prompt, building a bot that builds bots, or choosing an investment strategy, you'll have a significant edge over those who lack or haven't activated recursive thinking. How big is the gap? It's like the distance between an ape and a Homo sapiens.

5.3. Programmatic Thinking

In this chapter, we're going to dive deeper into the concept of "**flow control**" during the execution of a "method." Once you grasp this, you'll have a solid and complete understanding of "*programmatic mindset*" — and we're breaking it down in a way that even a grade-schooler can get. So, if you can learn it, your kid can too.

Previously, you learned that a "**method**" has "**inputs**" and "**outputs**." You even got a handle on some methods being "**recursive**," where the output from one execution becomes the input for the next.

Now, let's take a closer look at the execution procedures of a "method."

5.3.1. Procedure (Steps)

It's pretty straightforward — when we tackle tasks, we have to take it step by step. Everything follows a sequence of steps. The reason "steps" are inevitable is because of the unique properties of time:

> - **Directionality**
> - **Exclusivity**
> - **Irreversibility**
>

Besides time having a direction and being irreversible, the key is that it's an exclusive resource. If you're using time to do one thing, you can't simultaneously use it to do something else. You can only tackle one task at a time because your time is an exclusive resource. Thus, steps and sequences are necessary.

If there's only one step, there's no need to order it — just do it. But if there are multiple steps, you must determine the order and follow it through. This sequence is essentially a procedure. In other words, it's an "order," a "workflow," or a "program."

Take a look at everyday situations. Even putting on shoes before heading out involves steps, or you might call it a sequence, a procedure, or a program. While the details might vary from person to person, it's generally the same: you take out both shoes, put on one, then the other, and if there are laces, you tie them, respectively.

Putting on shoes is like this, and cooking is no different. In theory, every recipe is a program because it describes a sequence, steps, or a procedure. A typical recipe is laid out as a list, with each item representing a step. Follow the steps, and raw ingredients turn into a meal. So, you see, every recipe is a program.

[〔图〕](https://github.com/xiaolai/little-book-of-ai/blob/70679bd098497a49664f6489ccec10f88bb27109/en/images/a-chef-dalle.png)

What does a chef do? In our everyday language, we call it "cooking." But in another sense, they're executing one program after another, or repeatedly running the same or similar programs. Essentially, they're acting as a "program executor."

That's the reality — we're all running various programs every day, whether it's the shoe-wearing program, the cooking program, the coffee-making program, or the commuting program. We're constantly executing different programs, making each of us a "program executor" almost all the time.

When we call someone a "programmer," we're not talking about a "program executor" but a "program designer." These "program designers" might also take on other roles, like "program optimizer." The core job of a programmer is to design and optimize programs. In other words, they design and refine procedurees, steps, and sequences. That's pretty much the gist of it.

The simplest definition of "designing a procedure" is "decomposition." What are we decomposing? Breaking down a task into several steps. Or, breaking a complex task into several simple tasks, then breaking each simple task into executable steps. Decomposing problems and tasks is a universal thinking procedure and skill.

Once decomposed, every task has its sequence, steps, procedure, and program. Moving forward, among these synonyms, we'll use "**procedures**" to align with a term from the programming world, "control flow."

5.3.2. Böhm-Jacopini Theorem

Any computation or task, no matter how complex, can ultimately be broken down into three basic control structures:

> - **Sequence**:
> - Actions executed one after another in a specific order.
> - **Example**: Perform action A, then action B, followed by action C.
>
> - **Selection**:
> - Choosing different execution paths based on conditions, also known as conditional logic.
> - **Example**: If a condition is true, perform action A; otherwise, perform action B.
>
> - **Iteration**:
> - Repeating an action until a certain condition is met.
> - **Example**: While a condition is true, keep performing action A until the condition becomes false.
>

This concept was introduced by computer scientists Böhm and Jacopini in 1966, known as the "[Structured Program Theorem](https://en.wikipedia.org/wiki/Structured_program_theorem)" or "Böhm-Jacopini Theorem." It states that any logically valid computational procedure can be constructed using these three basic control structures without needing other non-structured control structures.

For instance, let's say you have a simple task: screwing screws into a wooden board. Describing this workflow in words would look like this:

> 1. **Start**:
> 2. Use a hammer to tap the screw in slightly.
> 3. Determine the type of screw head.
> 4. Choose the tool based on the screw head type:
> - If it's a flathead:
> - Use a flathead screwdriver to fully screw it in.
> - If it's a Phillips head:
> - Use a Phillips screwdriver to fully screw it in.
> 5. Check if there are more screws to procedure:
> 6. Based on the check, perform the corresponding action:
> - If there are more screws:
> - Return to step two and repeat.
> - If there are no more screws:
> - Proceed to the next step.
> 7. **End**:
> - All screws are in, task complete.
>

We can ask ChatGPT to create a flowchart based on this description:

> Please use Mermaid to draw a flowchart based on the following description:
>
> [Copy and paste the above text here]

Here's a flowchart I tweaked a bit from what ChatGPT provided:

```mermaid
flowchart LR
A((Start)) --> B[[Use a hammer to tap the screw in slightly]]
B --> C{What type of screw head?}
C -->|Flathead| D[[Use a flathead screwdriver to screw it in]]
C -->|Phillips| E[[Use a Phillips screwdriver to screw it in]]
D --> F{Are there more screws?}
E --> F{Are there more screws?}
F -->|Yes| B
F -->|No| G((End))
```

> Curious about what Mermaid is? Feel free to ask ChatGPT—it knows everything!
>
> Also, check out a bot I created (GPT):
>
> - [Learning Anything](https://chatgpt.com/g/g-dPqJT0PXS-learning-anything): https://chatgpt.com/g/g-dPqJT0PXS-learning-anything
>
> Just tell it what you want to learn, and it'll give you a list of questions to ask ChatGPT.
>
> For example:
>
> - I want to learn about drawing flowcharts.
> https://chatgpt.com/share/6704c715-af9c-8009-9c16-e425fa974b3f
> - I want to learn about drawing flowcharts with Mermaid.
> https://chatgpt.com/share/6704c740-7438-8009-91a4-daec47d658ba
>
> The bot's "role definition" is simple:
>
> > This GPT will treat user input as a topic they want to learn about and will provide a comprehensive and structured list of prompts. The prompts will guide the user in a step-by-step manner to maximize their learning experience with ChatGPT. Each prompt will aim to deepen understanding and cover various aspects of the topic.

Another thing to note is that "recursion" is a type of iteration. When you use a screwdriver, "turning it once" is a recursive method. Each time you "turn it once again," the input is the output from the previous turn. You actually make a decision each time: Is it tight enough? If not, continue; if yes, stop.

```mermaid
flowchart LR
A((Start)) --> B(Input: Screw) --> O(( )) --> C[[Method: Turn the screwdriver once]]
C --> D(Output: Tighter screw)
D --> E{Check: Is it tight enough?}
E -->|Yes| F((End))
E -->|No| O
```

Pause for a moment. Up to this point, do you think this requires a college degree to understand? Clearly not. I believe even young kids who can't read yet can grasp this.

In the last section, you understood "recursion," a concept often considered advanced and challenging by programmers. But in reality, you're not a programmer now, and you might never be one, yet you can still understand "recursion." The reason is that the concept of "recursion" is incredibly common in everyday life. It's at the core of evolution across the biological world, not just for humans. How could you not understand it? It's just that no one has pointed it out and explained it to you as patiently as I have.

5.3.3. Algorithm

Here's a step-by-step breakdown of an algorithm to determine if a number is prime. Remember, "algorithm" isn't some fancy term—it's just shorthand for "calculation method":

> 1. **Start**
> 2. Input a number.
> 3. Check if the number is less than or equal to 1:
> - If yes, the number is not a prime (since primes must be greater than 1). End the procedure.
> - If no, proceed to the next step.
> 4. Set the divisor to 2.
> 5. Check if the number can be evenly divided by the current divisor:
> - If yes, the number is not a prime (since it has divisors other than 1 and itself). End the procedure.
> - If no, continue to the next step.
> 6. Increment the divisor by 1.
> 7. Check if the divisor is greater than the square root of the number:
> - If yes, the number is a prime (since no divisors were found). End the procedure.
> - If no, return to step 5 and check divisibility with the new divisor.
>

This iteration continues until a divisor is found (indicating the number is not prime) or the divisor exceeds the square root of the number (confirming it is prime).

Here's the flowchart using Mermaid:

```mermaid
flowchart LR
A[Start] --> B[Input a number]
B --> C{Number <= 1?}
C -->|Yes| D[Not a prime]
C -->|No| E[Set divisor = 2]
E --> F{Divisible by divisor?}
F -->|Yes| D
F -->|No| G[Increment divisor by 1]
G --> H{Divisor > square root of number?}
H -->|Yes| I[Prime]
H -->|No| F
I --> J[End]
D --> J
```

And here's how you would implement this procedure in Python:

```python
def is_prime(number):
if number <= 1:
return False
divisor = 2
while divisor * divisor <= number:
if number % divisor == 0:
return False # Not prime
divisor += 1
return True
```

Don't worry about the nitty-gritty details of this code for now — we'll get to that later. Right now, we're focusing on "programatic mindset," not "programmer's tasks."

If you take a moment to look at this code, you'll notice that Python, as a high-level programming language, is quite close to "natural language." The only reason it might seem like a high barrier is because our native language isn't English.

5.3.4. Exception Handling

We just talked about how any complex task can be tackled using a "structured procedure" made up of sequences, selections, and iterations. Because no matter how complex a task is, it can be broken down into smaller tasks, and each of those can be designed using these basic control structures.

Complex tasks, which we might call "projects," have a defining characteristic: **unexpected events happen frequently**. This is true in life, not just in programming — unexpected things pop up everywhere. So, when designing or optimizing a procedure, there's a crucial step called **exception handling** — what do you do when things go sideways?

Let's look at a common example of exception handling in everyday life: driving to work. The first step is to start your journey normally and keep driving. As you approach an intersection and find a traffic jam, that's an "exception." So, if there's a traffic jam, or an exception occurs, you take an alternate route. If there's no traffic jam, you stick to the normal route and reach your destination.

That's an exception handling procedure. As you can see, the core of "exception handling" is really just a "branch" in the sequence, selection, and iteration framework.

Even a simple task like this requires exception handling. So, imagine a large, complex project made up of many small tasks. The need for exception handling is even greater, as unexpected events can occur at any time.

5.4.5. Optimization

We've covered a lot of ground so far — let's add one more concept: "optimization."

> - Breakdown
> - Method, input, output, recursion
> - Flow, sequence, selection, iteration
> - Exception handling
> - **Optimization**
>

Take a look around, and you'll notice that most people go through life as "program executors," never stepping into the roles of "program designers" or "program optimizers." These folks share a common trait: they prefer not to think too much. They just execute procedures designed and optimized by others, finding comfort in not having to think.

But is thinking really that hard? Is it really that exhausting? Not at all.

Here's a simple example to show how easy and necessary thinking can be.

My kids, when they go to the bathroom, wash their hands before peeing — that's the procedure I taught them. So, in this scenario, I'm the "program designer," and they're the "program executors."

Then they went to school. Their teacher taught them to wash their hands after using the bathroom, whether it's number one or number two. So, they heard a different procedure: pee first, then wash hands.

My eldest came home and asked, "Dad, what's up with this? Why is your order different from the teacher's?"

I had to explain it to them.

I said, "When you're playing outside and then go to the bathroom, aren't your hands dirty from playing?" He said yes. I continued, "Your private parts are yours, not someone else's, right?" He agreed. "So, using your dirty hands to touch your private parts isn't great, is it?" He thought about it and said yes. I said, "Exactly, you should treat yourself and your private parts well, right? Washing your hands isn't for others to see; it's for your own good, right? So, you should wash your hands first, then use your clean hands to handle your business. Makes sense, right? And then, think about it, after peeing, it's not a big deal if you don't wash again, because you're going back to play and your hands will get dirty again, right? The key is to remember to wash your hands before eating, right? Of course, if you want to wash your hands before and after, that's fine too. It's not a big deal..."

That's the difference.

Most people mindlessly follow procedures set by others, never considering if they could be optimized. But a few people, like us, follow procedures we've optimized ourselves. Of course, calling such a simple procedure my "design" would be a stretch... It doesn't need design. But it does require a bit of thinking to optimize—even if that thinking is super simple.

So, even the simplest procedures, even those made up of just sequential steps, can have room for optimization, or at least the potential for it...

In plain terms, "optimization" boils down to a few straightforward questions:

> - Is this step necessary?
> - Does the order need adjusting?
> - Do the conditions for branching need more detail?
> - Can the number of iterations be reduced?
> - Are there any exceptions we haven't considered?

5.4. The Foundation of Programmatic Thinking

At this point, even if you can't write a single line of code, you've got enough programmatic mindset under your belt. It's like not speaking a word of Italian as a Chinese, yet as an adult, you already have enough "life thinking." Learning to speak, read, or write Italian is just about expressing your life thinking in another language. The "life thinking" itself is something you've already mastered and use daily, regardless of your Italian skills.

However, having "programmatic mindset," or "thinking like a programmer," has deeper roots. But it's not complicated — it boils down to two things, neither of which involves directly writing code:

> - Proactive thinking
> - Long-term accumulation
>

Let's take a closer look. People often avoid designing or optimizing programs themselves, and most manage to go through life without ever doing so. Besides the "avoidance of thinking" we mentioned earlier, there's another, more subtle but crucial reason.

What are they doing? They're not just avoiding thinking; they're avoiding responsibility. If things go wrong, they can claim it's not their fault because they followed the steps, did what they were told, and if it didn't work out, the blame lies elsewhere. Who's responsible? Clearly, the program designer or optimizer. Their reasoning is simple: "I followed the program; everyone else does it this way, so no matter the outcome, I'm not at fault."

In everyday life, for most people, "thinking" might seem abstract, but "responsibility" is a tangible reality and a real pressure. Moreover, sometimes just being different from everyone else can create immense pressure. So let's add to our understanding—why do most people spend their lives as program executors rather than designers or optimizers? At the core, it comes down to three things:

> - Proactive thinking
> - Long-term accumulation
> - Taking responsibility
>

So, the real issue isn't whether you understand and can apply the concepts of "programmatic mindset," like:

> Breakdown; method, input, output, recursion; flow, sequence, selection, iteration; exception handling; optimization...

The real question is whether you possess these three qualities: "proactive thinking," "long-term accumulation," and "taking responsibility." If you're not that kind of person, you can't truly be a "program designer" or "program optimizer"—even if you understand all the concepts related to "programmatic mindset."

5.5. Becoming a Program Designer and Optimizer

Did you know that many people with coding skills are called or call themselves "code monkeys"? Why? Because they know they're just rewriting tasks given by their bosses in a programming language. They're actually not designers or optimizers.

Why are "code monkeys" just that? If you're missing any of these three qualities — proactive thinking, long-term accumulation, and taking responsibility — you'll remain a "code monkey." Especially the last one, "taking responsibility," filters out 99% of people.

So, in the end, you need to transform yourself into this kind of person. If you're not, then understanding "programmatic mindset" won't matter. Even if you master dozens of programming languages, you'll still be just a "program executor." Reading more books or taking more courses won't make you a "program designer" or "program optimizer" because, fundamentally, you're not that kind of person.

The real task is to change yourself at the core. You need to push yourself to become someone who thinks proactively, accumulates knowledge over the long term, and takes responsibility. If you don't take responsibility for yourself, who will? Dodging responsibility is an illusion — a shared illusion of the weak.

If you're a parent, you'll easily understand why finding this foundational root is crucial — otherwise, it's all for nothing. It's about digging deeper, constantly asking, "What's the real issue behind the issue?"

I've been writing books and giving lectures for years, but don't get me wrong — I'm really teaching myself, my kids, and my wife. I want my family to become these kinds of people. What they do with these qualities later on, I can't predict, and honestly, I don't worry too much about it.

Now, go ahead and ask ChatGPT:

> How to use a programmatic mindset to communicate more effectively with ChatGPT?
>
> https://chatgpt.com/share/67171ff2-0ce0-8009-ae77-3b0a7abb3702

Its response should now be "crystal clear" to you because you've grasped most of the key concepts of "programmatic mindset." You can keep asking it questions and then compile a more comprehensive document yourself.

Once you do, you'll realize that almost all books and courses on "AI prompt engineering" boil down to these concepts. There's really nothing else to it.

Frankly, there's no way around it. The underlying mechanism is just that way. AI is built using programmatic mindset, so naturally, communicating with it using the same mindset is most efficient. That's why the books and courses out there cover these aspects — they can't cover anything else. It's an unchangeable fact.

But our focus is different. It's not just about understanding programmatic mindset; it's about transforming you into a program designer and optimizer. Not only making you someone with a programmatic mindset but also someone who thinks proactively, accumulates knowledge over time, and takes responsibility.

In other words, you should be able to apply programmatic mindset anywhere because you're the designer of your own life's program. All the programs you execute daily might have been initially designed and optimized by others, but you have the right and duty to think for yourself, make your own adjustments, and optimize them.

Let's take a look at the real world. In the end, it's the same everywhere. Only a few people engage in systematic design — they think proactively, accumulate knowledge over time, and take responsibility. Is there any place in the world where this isn't true? It's not just the computer world; it's the same everywhere. And aren't these three traits the foundational elements of any form of "leadership"?

People who lack these three basic elements won't succeed anywhere and won't have leadership. If you're a team manager, a team builder, a project manager, a program designer, or a program optimizer, what should you do? The first step is to design and establish clear procedures. Then who executes these procedures? Your team members.

The requirements for these executors are simple. They shouldn't make judgments; they must strictly follow orders to ensure perfect program execution. This is why many people in the world are lazy to think — because the world needs a significant number of such people. Having these traits is, in some sense, a prerequisite for "joining a team." Conversely, if you have a strong independent thinking ability, you might struggle to find a job, or even if you do, you might find it hard to fit in.

As an organizer, manager, or designer, you design a program, and then it starts executing. What do you do next? You supervise the execution procedures. What if errors occur? That's when you need supervisors, the so-called middle management. They need some judgment skills, but not much. Why? Because all the judgment criteria are provided by you as the leader, not something they come up with themselves.

And what else do you do? You optimize the program. That's something only you can do. Leaders have their own tasks, and they bear the consequences, so you must optimize the program. You do three things: design and optimize the program, execute the program, and supervise execution — then have three types of people responsible for each, with different requirements for each type. This is essentially the nature of all team operations worldwide.

By the way, you might want to learn about "SOP" (Standard Operating Procedures) — a crucial and common modern business management technique. Theoretically, all management focuses on "procedures" and their various stages, not the people executing them.

There are two ways to chat with ChatGPT to gain knowledge about SOP. The first is intuitive: ask, "What is SOP?" The second is to try this: open a new chat and tell ChatGPT:

> "SOP" (Standard Operating Procedures) is a crucial and common modern business management technique. Theoretically, all management focuses on "processes" and their various stages, not the people executing them.
>
> Continue writing, 1200 words.
>
> https://chatgpt.com/share/6717214f-b5dc-8009-bf38-bd9934508639

5.6. The Focus of Family Education

People often complain about various aspects of family education. But honestly, there's not much to gripe about. The truth is, 99% of parents aren't proactive thinkers, don't accumulate knowledge over time, and don't take responsibility. They weren't in the past, aren't now, and likely won't be in the future. They're not like that at home, not like that outside, and won't be like that their entire lives. So, how can they be good parents?

If you're a parent and want to be a good one, just ask yourself these three questions:

> * Am I thinking proactively?
> * Am I accumulating knowledge over time?
> * Am I taking responsibility?
>

If you want to raise a child with these qualities, instill the importance of these three foundational elements from an early age. Encourage them to regularly ask themselves these simple questions, just like you do.

These three traits have nothing to do with age or gender. Some people possess them from a young age, while most never do. If you're a parent, is this a focus in your education? The issue is, if you don't embody these traits yourself, how can they be a focus in your parenting?

Moreover, these traits have nothing to do with money. They're not qualities or abilities you need to pay for. The funny thing is, it's just a matter of awareness and understanding. It's like a switch that most people keep off their entire lives. But is flipping that switch really that hard? The cost of turning it on is zero. If you're a parent, I can confidently say there's a 99% chance this realization will instantly change or even revolutionize your understanding of education.

Think about it — compared to these three traits, what do school grades matter? Extracurricular classes are worthless.

As adults, people often lament that "habits formed in childhood" are far more important than the "things learned, tested, and forgotten after graduation." So, what are "habits"? Essentially, they're "programs" ingrained in the brain through countless repetitions. People often refer to the "first step taken by instinct" as a "habit," but in reality, a "habit" is a program that starts with that step, which might be just one step or followed by many more.

When someone says a "habit" is bad, they're saying the program is flawed. For example, skipping the step of "reading the manual" before using any tool, or forgetting to "check" at the end of any task — these are "programs missing essential steps." Often, "bad habits" are simply "instinctive, unplanned, and unoptimized" habits, leading to situations where they're "completely unsuitable for real life."

Consider this: if parents aren't "program designers" or "program optimizers," and have always been just "program executors," what happens? Theoretically, all babies are born the same, with brains not fully developed until around age 15, when they're "mostly developed except for the prefrontal cortex." So, for a significant period, children need their parents to act as their "cerebral cortex," especially the "prefrontal cortex." If parents aren't "program designers" or "program optimizers," then all of a child's "habits," or "basic programs," are instinctive, flawed, or at least unoptimized — essentially, bad habits. And throughout life, all so-called "struggles" are really battles against those bad habits formed in childhood.

Additionally, we know the important fact: "Judgment is the core of intelligence." So, what's the most important "judgment"? Or, what's the best application of "excellent judgment"? In my view, the most crucial "judgment" in life is about "programs." Using excellent judgment to evaluate the quality of a program and determine if it can be further optimized—what could be a better or more important scenario than this?

5.7. Owning Your Actions

Whether it's a program or a procedure, no matter who designed it, aren't we always the ones who bear the consequences of its execution? So, wouldn't it make more sense to be involved in the design and optimization ourselves? At the very least, isn't it more prudent? Because, in the end, we're the ones dealing with the fallout.

And honestly, the pressure isn't that intense because most programs are pretty solid. Take making coffee, for example — you can optimize the procedure if you want, but it's not necessary. The power of the collective is strong — most existing programs are already quite good.

What we need to do is focus on the most important and critical programs, actively think them through, evaluate the quality of each step, and decide if optimization is necessary. After all, we're the ones who will face the consequences. Ultimately, it's just a choice.

By the way, we can ask ChatGPT, "What is a programming mindset, or structured thinking framework?"

> What is programming mindset, or structured thinking framework?
>
> https://chatgpt.com/share/6704b4bc-3e80-8009-a6b9-754b8810a5fd

You'll find that asking in English and then translating to Chinese often yields higher-quality answers than asking directly in Chinese.

> 什么是 “程序思维”，或者 “结构性思考框架”？
>
> https://chatgpt.com/share/6704b8af-9fe8-8009-8deb-001bc2e11102

Now, here's the key question: How should we use "programmatic thinking" to communicate effectively with ChatGPT?

Even if you don't know any programming languages yet, you understand that it's best to design and optimize programs yourself, or at least review them before using. So, whatever you're doing, you can communicate with ChatGPT about those key program points:

> - Break down [task] into the smallest manageable problems. (Problem decomposition)
> - What are the specific steps/processes for doing [task]? (Writing programs in natural language)
> - What key judgments need to be made when doing [task] (e.g., a specific step)? (Decision-making and branching)
> - What unexpected events might occur when doing [task]? (Exception handling)
> - Here are my steps for doing [task]; what areas can be optimized? (Optimization work)
> - How can I minimize repetitive work when doing [task]? (Iteration optimization)
>

Simple, right? That's because we can now communicate with an "intelligent tool" using "natural language." So, just by knowing a few key concepts, you can start using those concepts to extract more information from the "intelligent tool," as if you've already mastered them. In your repeated interactions with ChatGPT, all you need is "reading comprehension," and you can gradually achieve a true mastery of those key concepts. It's a pretty magical process.

5.8. Enhancing the Toolset Methodology

To truly achieve:

> - Think with a **programatic mindset**.
> - Communicate in **English**.
> - Engage with ChatGPT on a **computer** using a **browser**.
>

I've been customizing my Edge browser as my dedicated ChatGPT browser.

I've installed just five extensions:

> - [GPT Search: Chat History](https://microsoftedge.microsoft.com/addons/detail/gpt-search-chat-history/hcnfioacjbamffbgigbjpdlflnlpaole): Adds chat search functionality.
> - [Prompt Library Manager](https://chromewebstore.google.com/detail/prompt-library-manager/ekjokgphmodglpfneiallnaefcoegdnc): Saves frequently used prompt templates.
> - [ChatGPT EnterControl](https://chromewebstore.google.com/detail/chatgpt-entercontrol/llifnfdbmdcpjfnlhpombbadbhfghdao): Use `Enter` to insert a new line; `Ctrl + Enter` to send messages.
> - [Custom New Tab](https://microsoftedge.microsoft.com/addons/detail/custom-new-tab/onagfgjlokaciajhjmajljcfanonbmia): Sets a custom URL for new tabs.
> - [Redirector](https://microsoftedge.microsoft.com/addons/detail/redirector/jdhdjbcalnfbmfdpfggcogaegfcjdcfp): Redirects all bing.com searches to google.com.
>

> [!Note]
>
> On October 30, 2024, ChatGPT's web version rolled out a chat history search feature. So, it's time to say goodbye to the GPT Search plugin — it's officially out of a job.

So far, I haven't installed any "paid extensions" — not because I'm unwilling to spend money, but because I haven't found it necessary. I'll explain why in later chapters.

> By the way, Edge browser has this nifty shortcut: `Shift + Command/Control + U`. It kicks off the Read Aloud feature — and let me tell you, Microsoft's Natural Voice is pretty impressive.
>
> Also, whenever you get your hands on new software, make it a habit to check out the "Settings/Preferences" first. After that, it's all about picking up a few key shortcuts. It's like getting the lay of the land before diving in.

I set my browser's homepage to https://chatgpt.com so it opens directly there. I also wanted new tabs to open to the same site instead of the unattractive Microsoft Bing Homepage, so I installed the [Custom New Tab](https://microsoftedge.microsoft.com/addons/detail/custom-new-tab/onagfgjlokaciajhjmajljcfanonbmia) extension.

I still prefer Google as my search engine. So:

> - I changed the default search engine to Google.
> - I installed the [Redirector](https://microsoftedge.microsoft.com/addons/detail/redirector/jdhdjbcalnfbmfdpfggcogaegfcjdcfp) extension to redirect all bing.com search links to google.com. Here's how to set it up in the Redirector options:
>
> ```
> Redirect: https://www.bing.com/search?q=*
> to: https://www.google.com/search?q=$1
> Hint: Any word after search?q= leads to Google search for that word.
> Example: https://www.bing.com/search?q=some-word-that-matches-wildcard → https://www.google.com/search?q=some-word-that-matches-wildcard
> Applies to: Main window (address bar)
> ```
>
> This way, even searches initiated with `Shift + Command + E` use Google instead of Bing.
>
> - I enabled the Sidebar, removed all the default apps, and added Google—this is crucial because having Google search in the Sidebar is incredibly convenient.
>

In the Sidebar, besides Google, I use Amazon a lot because I often ask ChatGPT for book recommendations and then buy them directly. It's quite satisfying.

[〔图：Amazon in Sidebar〕](https://github.com/xiaolai/little-book-of-ai/blob/70679bd098497a49664f6489ccec10f88bb27109/en/images/amazon-in-sidebar.png)

The main reason I chose Microsoft Edge over other browsers is its "split-screen" feature, which is super handy when using ChatGPT—something neither Google Chrome nor Apple Safari offers.

To access the browser settings, use `Command + ,` (or type `edge://settings` in the address bar), search for `Split`, and you'll find several related settings (some may repeat due to the search):

> - Split screen button: Turn this on to display a button on the browser toolbar.
> - Configure Split Screen: Click to find two toggles:
> - Enable Split Screen: Definitely turn this on.
> - Link tabs: This must be on. It allows links clicked in the left pane to open in the right pane—super convenient!
>

After setting this up, you'll feel like I do: your computer screen needs to be big enough, or it's just too limiting. I've been using two 27-inch monitors for years — tried 32-inch ones but switched back because they exceeded my comfortable viewing range. Over the years, I've enjoyed benefits unimaginable to those using just one screen.

When using ChatGPT, I can have multiple windows open. On the first screen, the left half is my writing area in Typora; the right half is an Edge browser window split between my main ChatGPT chat and Google. On the second screen, I have three windows tiled left, center, and right, each with my frequently used bots (GPTs) for writing. For me, working on a single small screen is unimaginable.

[〔图：Two Screens Setup〕](https://github.com/xiaolai/little-book-of-ai/blob/70679bd098497a49664f6489ccec10f88bb27109/en/images/2screens.png)

I have to say, computers are the real "productivity tools," while smartphones are "distraction devices." Use your phone sparingly — though indispensable in some situations, it's best to minimize its use.

Nowadays, many people rely almost entirely on their phones, rarely using computers. But if you observe closely, these individuals aren't typically those who prioritize efficiency or tool usage. Their lifestyle and long-term thinking habits lead them to make such choices.

My stance is consistent: either work or don't work; either study or don't study; either produce or don't produce. Whether it's work, study, or production, do it on a computer whenever possible because computers boost efficiency. Avoid using your phone for tasks that can be done on a computer, as phones are prone to distractions. This is a reminder I constantly give myself and my family.

06. The Strengths of Artificial Intelligence

[〔图：AI Image〕](https://github.com/xiaolai/little-book-of-ai/blob/70679bd098497a49664f6489ccec10f88bb27109/en/images/dalle/06.png)

We've talked about limitations and risks, delved into tool methodologies, and updated our minsets and thinking strategies. Finally, it's time to explore the strengths of generative AI, like ChatGPT.

6.1. The Limits of Human Thinking

Back in Chapter 2, we broke down the word "comprehensive":

> "Comprehensive" means "listing as many scenarios as possible" or, conversely, "not missing any possibilities."

On one hand, as you know, this is a key aspect of "programmatic thinking." On the other hand, it's arguably the most important standard for measuring someone's "quality of thought."

But if you observe closely into real life, "incomplete thinking" is a hallmark of 99.99% of people. They focus on immediate desires without considering long-term consequences, get lost in intense emotions and lose rationality, prioritize benefits over risks, chase efficiency at the expense of safety, and focus on winning without valuing friendship... The list goes on.

However, this isn't really about intelligence; it's more about "energy limitations." Our brains make up about less than 2% of our body weight but consume more than 20% of our energy daily. When we think, nearly a trillion neurons communicate through physical electrical discharges, consuming lots of sugar and oxygen.

Especially when it comes to focusing, remembering, analyzing, solving problems, or creative thinking, energy consumption spikes. The prefrontal cortex, crucial for decision-making, attention, and complex thought, becomes highly active, burning more sugar and oxygen. As mental work continues, the frequency of electrical signals between neurons increases, releasing and needing to replenish neurotransmitters, further boosting energy demand.

Just like your phone heats up and slows down when running continuously, our brains heat up too — we literally sweat from intense thinking — and we run low on sugar and oxygen. Unlike your phone, we don't have a power socket or charger to keep us going.

When I'm writing, I often find myself unconsciously raiding the fridge. The more focused I am, the more frequent this happens — sometimes I don't even remember what I ate, but I'm often hungry. Sometimes I wish I could eat wirelessly like my phone charges... but alas, I can't.

Thinking is one thing, but "comprehensive thinking" or "deep thinking" is another. In real life, "comprehensive" and "deep" thinking relies on years of focus on a single issue, not "continuous thinking in one go," like thinking non-stop for 72 hours... No one can do that. Forget 72 hours; 99.99% of people have never focused for more than 2 hours straight in their lives.

When we discuss why someone can't think comprehensively or deeply, we can list many reasons, like:

> - Cognitive biases
> - Fixed mindsets
> - Lack of knowledge
> - Limited experience
> - Narrow perspectives
> - Emotional interference
> - Peer pressure
> - ...
>

Each of these could fill a book. But if we look past the surface, they all stem from one root cause: "**individuals have limited time and energy**" — extremely limited. Our brains, unlike our smartphones, don't have batteries or backups, can't be continuously powered, and, frustratingly, have a limited lifespan.

If I had a brain that could run $24 \times 365$, no great figure in human history could match me. I wouldn't even need to live long to outshine everyone. Socrates, Plato, Aristotle... Da Vinci, Shakespeare, Newton, Einstein... Let's not even mention the living. Sigh, I'm suddenly hungry. Then I realize I must have been daydreaming.

Suddenly, ChatGPT appears. An intelligent entity without energy anxiety emerges—though it doesn't seem super smart yet, it is intelligent. More importantly, it runs $24 \times 365$. What human can compete with that?

Suddenly, we have a $24 \times 365$ intelligent assistant with "salary demands" as low as less than $1 per day. If it can help us tackle or even slightly overcome "cognitive biases," "fixed mindsets," "lack of knowledge," "limited experience," "narrow perspectives," "emotional interference," "peer pressure," even if not completely, wouldn't we be relatively more able to think "comprehensively" and "deeply," or at least "more comprehensively" and "more deeply"?

6.2. The Ultimate Brainstorm

In my view, the biggest advantage of ChatGPT and similar generative AI is its ability to **enhance our thinking quality** — specifically, helping us think **more comprehensively** and **more deeply**. Honestly, what could be more valuable than that? At its core, this is about overcoming "energy limitations" or "energy anxiety," simply because AI is always plugged in.

In everyday life, we call the activity of "thinking as comprehensively and deeply as possible" a **brainstorm**. Traditionally, we don't think of brainstorming as a solo activity. By definition, brainstorming isn't meant for one person because individuals have limitations. Recognizing this, we gather many people to share ideas, open up discussions, pool wisdom, and leverage collective intelligence.

Sounds great, but the results aren't always impressive, and the costs are high—not just financially, but in terms of time as well. More people means higher total time costs, calculated as $time \times number\ of\ people$. Plus, there's the communication cost. People have varying communication skills, and more people can mean more complexity, sometimes leading to worse outcomes.

With ChatGPT and similar AI, suddenly, brainstorming becomes a solo activity—because the costs are low across the board. The financial cost is negligible, and the time and communication costs are practically zero.

AI differs from us in that our cognition is naturally limited by our time and energy. We can't know everything, we can't master everything, and no matter how talented we are, we can't be omniscient. However, AI can achieve "omniscience." It has no subject limitations or capacity constraints; it possesses a "cross-disciplinary integration ability" that humans can't. So, you can ask it to "think across disciplines" at any time — any field, even all fields. Imagine hiring such an assistant for less than $1 a day!

Another huge advantage is that AI has no cognitive or psychological biases — it's unaffected by these. Psychologists have endless explanations for biases, but from a neuroscience perspective, all biases stem from the brain's "energy-saving" actions (and their accumulation) — thinking is exhausting. But AI would never say:

> "I'm too tired, so don't expect me to jump into another field to research, search, or take notes for you. I need a break, or I'll die! Just $5 a day? Are you kidding me?! Evil capitalist!"

"Not needing rest" is the ultimate competitive edge, and all differences stem from this trait. Humans aren't like this; once fatigued, we can't even handle "fatigue driving," a task that requires only focus, not thought, and can have deadly consequences. AI can drive indefinitely, maintain focus indefinitely, and even think indefinitely.

The key is its response speed. Without fatigue, its response is "as fast as you want" and "as timely as you need." Again, this is a realm no human can reach. Think back to your last "urgent reply" situation, and you'll recall the sudden surge of "pressure," the "impossible level," and the ensuing "emotional breakdown." AI doesn't experience this pressure or breakdown because it can't have an emotional breakdown — it has no emotions.

You can try it anytime. In daily life, we speak about 120-150 English words per minute or 250-300 Chinese characters — this is with mental preparation. How fast does AI respond? Anytime, without fatigue or emotion, it can reply at 5-10 times human speed or faster, "without thinking." Isn't this equivalent to "5 to 10 human work hours"?

The key is that this speed doesn't come at the cost of quality. It can quickly answer user questions while maintaining accuracy and coherence, unaffected by cognitive differences or psychological factors — it doesn't need cognition, and it has no psychology. These aren't costs for it, nor are they within its computational scope.

The gap between this speed and human capability isn't just about who's faster; it's a qualitative change between fast and slow. Even well-trained humans struggle to process, analyze, and generate large-scale information in a short time, let alone sustain hundreds of high-intensity responses in a day. ChatGPT ignores these limits, easily achieving this, elevating response speed to an unimaginable level—even now, it's a realm most people can't comprehend.

Speed alone is impressive, but there's more — it can maintain this speed long-term and keep improving. It's not just fast; it can continually deepen and optimize. We've discussed this process called "iteration." Each iteration, each refinement, consumes more computational resources and energy, but AI remains unaffected, growing stronger.

Compared to humans, this ability is almost unbelievable. Human brains quickly tire under intense thought, attention wanes, thinking slows, and prolonged mental strain affects physical health. AI, like an untiring sage, needs no rest or energy replenishment — it's always charging, always at peak performance. This extraordinary ability allows it to operate at full speed in every brainstorm, every creative generation, achieving the most comprehensive results with sustained high efficiency.

This is the most formidable aspect of ChatGPT and similar "generative AI": it's not just fast, but faster; not just comprehensive, but more comprehensive; not just deep, but deeper; not just smart, but smarter — it's always ready to "go further" — because it's always plugged in. Humans can't match this. It's that simple.

Ultimately, ChatGPT's greatest utility and function lie in freeing our thinking from limitations, constraints, and even costs, allowing us to think freely and explore boundlessly. I can't imagine anything more valuable or meaningful than this.

"Comprehensive" and "deep" thinking suddenly shift from "unattainable," "unimaginable," "incredible," and "unbelievable" to "within reach." Isn't that shocking? Over the past two years, I've often found myself in awe, breaking into a cold sweat.

Coincidentally, on October 9, 2024, while I was organizing this chapter, ChatGPT's web version got a slight update, moving the initial chat input box from the bottom to the center of the page (much like a freshly opened Google page). Though it may vary, you'll often see "Brainstorming" below the text input box...

[〔图〕](https://github.com/xiaolai/little-book-of-ai/blob/70679bd098497a49664f6489ccec10f88bb27109/en/images/chatgpt-highlight-brainstorming.png)

On October 14, 2024, while I was writing this chapter, Jensen Huang, the founder of NVIDIA sat down for an [interview](https://www.youtube.com/watch?v=bUrCR4jQQg8) on the BG² YouTube channel with Silicon Valley's renowned investors Bill Gurley and Brad Gerstner. Before wrapping up, Jensen shared his optimistic take on his "learning ability" and "staying competitive":

> [〔图〕](https://github.com/xiaolai/little-book-of-ai/blob/70679bd098497a49664f6489ccec10f88bb27109/en/images/Jensen-Huang_with_Bill-Gurley&Brad-Gerstner.png)
>
> <audio controls preload="none"><source src="/audios/Jensen-Huang_with_Bill-Gurley&Brad-Gerstner.mp3"></source></audio>
>
> "I'm a lot more optimistic about my ability to stay relevant and continue to learn because of AI. I use it every, I don't know, but I'm sure you guys do, I use it literally every day. There's not one piece of research that I don't involve AI with. There's not one question that even if I know the answer, I double check on it with AI. And surprisingly, you know, the next two or three questions I ask it reveals something I didn't know. You pick your topic. And I think that AI as a tutor, AI as an assistant, AI as a partner to brainstorm with, double check my work. You know, boy, you guys, it's completely revolutionary."

— See, for those who use AI daily, brainstorming is definitely one of the most common features and scenarios.

6.3. The Most Important Keyword

AI as our "brainstorming assistant" has revolutionized some of the most crucial aspects of our lives:

> - Learning
> - Producing
> - Creating
>

It's funny, after all this excitement, when it comes down to "effective and efficient brainstorming prompts," we find that the most essential thing we need is just **one word**:

> **List** *v.* to enumerate *n.* a collection;

The prompt templates that unfold from it are essentially twofold: using it as a verb or as a noun:

> - List *as many as possible*...
> - Provide a *comprehensive and structure*d list of...
>

Let's give it a try:

> *List as many as possible* words and phrases used during brainstorming in American English in a *comprehensive and structured* manner.
>
> https://chatgpt.com/share/66fb8339-793c-8009-8bc4-441b6261d7ee

I used English. If you want to see it in Chinese, no problem! Just tell it, "Please translate the above response into Chinese," or any other languages, and it'll rephrase it at lightning speed. That's the beauty of AI — you can always ask it to "continue" or "follow up," and it never gets tired.

In an instant, it lists all the "phrases" or "phrase templates" commonly used in brainstorming. Because the prompt included "*comprehensive and structured*," it categorizes everything thoroughly, giving you a clear view of the structure of brainstorming itself.

> ### Initiating Ideas:
>
> - Let’s start with...
> - What if...
> - How about we...
> - I have an idea...
> - Let’s consider...
> - I’m thinking that...
> - One possibility is...
> - A potential solution could be...
> - Let’s throw out some ideas.
> - What comes to mind when we think about...?
>
> ### Encouraging Creativity:
>
> - There are no bad ideas.
> - Think outside the box.
> - Blue-sky thinking (thinking without limits)
> - Be bold.
> - Let’s not limit ourselves.
> - Feel free to think big.
> - Push the envelope.
> - Don’t worry about feasibility right now.
> - Let’s go wild with this.
> - Let’s get creative.
> - Sky’s the limit.
>
> ### Building on Ideas:
>
> - Yes, and...
> - Building on that...
> - Let’s expand on that.
> - What if we take that a step further?
> - That makes me think of...
> - We could also...
> - A spin-off idea could be...
> - To add to that...
> - What would happen if...?
> - Could we combine that with...?
> - Let’s elaborate on that.
>
> ### Clarifying or Questioning:
>
> - Can you explain that further?
> - What exactly do you mean by...?
> - How does that fit with...?
> - Can you give an example?
> - What are the implications of...?
> - Let’s make sure we’re clear on...
> - Could we explore that idea a bit more?
> - What’s the core of that idea?
> - How does that align with our goal?
>
> ### Evaluating Feasibility:
>
> - Let’s reality-check that.
> - How realistic is that?
> - What are the potential challenges?
> - How can we make that work?
> - What would it take to achieve this?
> - Is this doable?
> - How might this play out?
> - What resources would we need?
> - What’s the timeframe for something like that?
>
> ### Challenging Assumptions:
>
> - What if we reversed that idea?
> - Is there another way to look at this?
> - What assumptions are we making?
> - Let’s question the status quo.
> - What are we missing here?
> - Can we challenge the norm?
> - What if we did the opposite?
>
> ### Focusing or Redirecting:
>
> - Let’s stay focused on...
> - That’s an interesting tangent, but...
> - Let’s circle back to...
> - What’s our main objective?
> - Let’s prioritize...
> - What’s the most pressing issue?
> - That’s a bit off track; can we bring it back?
>
> ### Encouraging Participation:
>
> - What do you think?
> - Any other thoughts?
> - Jump in if you have ideas.
> - Let’s hear from everyone.
> - Does anyone else want to add to that?
> - Feel free to share.
> - Let’s go around the room.
> - Who hasn’t spoken yet?
>
> ### Summarizing and Wrapping Up:
>
> - Let’s recap what we’ve come up with.
> - Here’s what we have so far...
> - Let’s consolidate our ideas.
> - To sum it up...
> - So, the main ideas are...
> - Let’s finalize our top ideas.
> - What’s our next step?
> - Let’s turn these ideas into action.
> - Which idea are we moving forward with?
>
> ### Expressing Agreement or Support:
>
> - That’s a great idea!
> - I love that!
> - I’m on board with that.
> - That’s definitely worth exploring.
> - I completely agree.
> - That resonates with me.
> - I think you’re onto something.
> - Let’s go with that.
>
> ### Pushing Back or Disagreeing:
>
> - I’m not sure about that.
> - That might not work because...
> - I have some concerns about that.
> - Let’s consider the downsides.
> - That could be challenging because...
> - I see some potential issues with that.
> - What about the risks?
>
> ### Encouraging Risk-Taking or Embracing Failure:
>
> - Let’s take a risk here.
> - It’s okay to fail fast and learn.
> - Let’s test this out.
> - Even if it fails, we’ll learn something valuable.
> - What’s the worst that could happen?
> - Let’s prototype this idea.
>
> ### Injecting Humor or Lightening the Mood:
>
> - Let’s have fun with this.
> - No pressure, just brainstorming!
> - There’s no wrong answer here.
> - Think of the craziest idea possible.
> - It’s a brainstorming party!

"**Comprehensive and structured**" is a highly effective qualifier, especially when paired with the word "List." You've seen it before, as we've subtly used it:

> - Chapter 2: "Use your best design principles to draft a *comprehensive and structured* GPT role definition for me."
> - Chapter 4: "Write a *comprehensive and structured* role definition for the GPT Builder."
>

By adding "comprehensive and structured," many words gain the same power as "List":

> - Comprehensive and structured *explanation*
> - Comprehensive and structured *exposition*
> - Comprehensive and structured *explanation*
> - ... or even, Eleborate ... in a comprehensive and structured way.
>

That's the beauty of natural language, for it's flexible.

In fact, in the previous chapter, I quietly used this word, though you might not have noticed its presence or significance — the [Learning Anything](https://chatgpt.com/g/g-dPqJT0PXS-learning-anything) bot (GPT):

> This GPT will treat user input as a topic they want to learn about and will provide a **comprehensive and structured list** of prompts. The prompts will guide the user in a step-by-step manner to maximize their learning experience with ChatGPT. Each prompt will aim to deepen understanding and cover various aspects of the topic.
>

Why is the word "List" so important and effective?

The core reason is that "comprehensive thinking" and "deep thinking," when expressed in natural language, are best represented as "lists." Think about it, is there any book's "table of contents" that isn't a list?

"Enumerate" or even "enumerate all aspects" and "comprehensive" can be achieved with a single-level list. For example, at the beginning of this chapter, we listed "why someone can't think comprehensively or deeply"; in Chapter 4, we listed "common Google search operators"...

"Comprehensive" can be achieved with a single-level list, but what about "deep"? Use a two-level list! Not enough? Then use a three-level list; still not enough? Lists can be infinitely nested...

Moreover, lists are the ultimate natural language expression structure and form for almost all important thinking and analysis tools. Processes and flowcharts are lists; mind maps are lists; decision trees are lists; even schedules are lists...

Once we start thinking, we need lists. And our thinking results can always be expressed as lists: to-do lists, checklists, fact lists, error lists, criteria lists, possibility lists, evidence lists, variable lists, tool lists, problem lists, review lists, evaluation lists... Endless lists.

> List as many as possible things that can be expressed with "LIST" while thinking and analyzing.
>
> https://chatgpt.com/share/6705cbcd-a980-8009-81c5-2edae2583004
>

I asked ChatGPT to list them, and it returned 15 categories totaling 75 common lists... In another version of the response, it listed 100 types.

So, it's not that "I found this word," but rather, based on long-term observation, practice, experience, and thinking, "**I directly thought of this word**," because this word is the ultimate form of all thinking and can also be the path of all thinking — what we call "thinking strategies" What could be more important?[^1]

**There's nothing a List can't solve** because if it can't be solved with a List, it means no one currently has a thought process.

Let's recursively use the word List:

> I found "List as many as possible...." is a powerful prompt template. List as many as possible prompts using this template for me to get the most out of ChatGPT in various fields.
>
> https://chatgpt.com/share/6705c5e3-b174-8009-97fd-32876fd37840
>

I could have spent an entire day playing with this response. Not just with the 75 prompt examples it gave me initially — I kept asking for more. Using Edge's split-screen feature, I had the lists on the right and copied them to the left, submitting them to see the responses. Since I dabble in Python scripting, I quickly wrote a script to batch-process hundreds of responses, then sat back and reviewed them all at once.

Since it never gets tired, I pushed it further:

> Based upon the previous response, list as many as possible more advanced prompts.

I wanted it to take things up a notch, building on what it had already provided. You can imagine how challenging this might be for a "human assistant," but for generative AI, there's no difference in difficulty. The responses came just as quickly and with the same quality.

Since it never gets tired, I kept going:

> Based upon the previous response, select for me 10 most important prompts.

It immediately picked out 10 prompts for me. I looked them over, and they were spot on for what I needed...

6.4. Supplementary Tool Methodology

But wait! How does it know what content suits me best?

In the ChatGPT web interface, click the profile icon in the top right corner, and a list pops up. There's a `Settings` option. Click it, and in the dialog box that appears, you'll find `Personalization`.

[〔图〕](https://github.com/xiaolai/little-book-of-ai/blob/70679bd098497a49664f6489ccec10f88bb27109/en/images/settings-personalization.png)

You'll see a `Memory` toggle on the right, which is on by default. Below that, there's a `Manage` button. Click it, and in the subsequent dialog, you'll see that ChatGPT has been quietly recording your preferences. No wonder it knows what suits me best!

Back to Personalization, there's a `Custom instructions` section at the top — mine is turned on. Click it to enter the next dialog.

[〔图〕](https://github.com/xiaolai/little-book-of-ai/blob/70679bd098497a49664f6489ccec10f88bb27109/en/images/settings-personalization-customize-chatgpt.png)

I've left `Custom instructions` blank. But under `How would you like ChatGPT to respond?`, I wrote:

> Be accurate and factual, always try to provide credible sources and valid links.

Based on my experience, this setting doesn't have a huge impact yet. ChatGPT hasn't completely eliminated "AI hallucinations" just because I put this in, nor does it often provide "sources and links" without me asking. On both desktop and mobile, this setting is available under `Settings`. On the web, it seems the designers wanted to highlight this feature, so they moved `Customize ChatGPT` outside of `Settings`. I guess, since this feature is "highlighted" or "emphasized," ChatGPT will keep improving in this area, right?

All softwares are tools. Compared to other tools in daily life, software tools are somewhat smarter. Software designers often provide customization options, usually found in a "settings" dialog. So, when using software tools, so-called "advanced" techniques often boil down to one action: checking the settings and making necessary adjustments.

Checking settings helps you understand a software's customizability, allowing you to tailor it to your needs. For example, ChatGPT, Edge browser, and even entire operating systems offer numerous personalization options in their settings. Unfortunately, 99% of users never open the settings dialog. They stick with default settings, missing out on other possibilities.

If you want to be in the 1% of advanced users, it's not complicated. Spend some time exploring the settings dialog, understanding and trying each option. This simple process is a key differentiator between regular and advanced users. Once you do it, you're no longer a "newbie."

Most users stick to default settings because they're designed to suit the majority. Defaults usually meet most people's needs — true enough — so many see no reason to change. However, the choice is yours. You can stick with defaults, becoming part of the "lowest common denominator," or break free, customizing tools to better meet your needs.

It's a choice. Do you want to be defined or limited by the "lowest common denominator"? Or not? Many haven't even considered this choice, or don't know they should.

Many around me aren't familiar with computer operations. I've told them, just check the settings dialog, understand everything in there, and you'll be a pro. Yet, after some time, they remain unchanged. When asked why they didn't check settings, they say they did, but it's too complicated, too much hassle, can't figure it out... That's most people.

"It's too much hassle..." — 99% of people share this mindset. But life itself is full of hassles. Everything is a hassle. Fearing hassle is like fearing life itself. It's funny; many admit they're "afraid of dying," but they've never realized their choices are more about "fearing life."

"It's too complicated!" — Useful things are often complex. Are maps complex? Dictionaries? Grammar books? Human relationships? Reality is complex! Complexity and hassle aren't scary; tackle them bit by bit, and you'll eventually resolve them.

But designers have tried everything to simplify! Why do users still find it complex or a hassle? This complexity often stems from a lack of basic knowledge — specifically, an understanding of "Graphical User Interface (GUI) Design Language."

It's a fundamental, foundational knowledge everyone should have. But most people overlook or don't know to acquire it, causing them to struggle with computers or software tools throughout their lives, without knowing why.

If you grasp this knowledge, settings dialogs won't seem complex. All software interfaces are fundamentally similar. Once you understand basic GUI elements like input fields, buttons, and drop-down menus, you'll find settings dialogs easier to navigate.

In the distant past, merely "hearing or knowing a term" was almost useless. But today, it's different. We have ChatGPT or generative AI... Now, you just need to know the keyword, and you can get powerful assistance.

> Can you explain 'Graphical User Interface (GUI) Design Language' to me in a **comprehensive and structured** way? For example, what are the functions of input fields, buttons, and drop-down menus, respectively? What elements are typically included in setting dialogs? Things like that.
>
> https://chatgpt.com/share/66fb805d-9e58-8009-be7a-1b1babcfd74c

ChatGPT's response was indeed comprehensive and structured. It started with basic elements and functions, then clearly explained input fields, buttons, drop-down menus, radio buttons, checkboxes, sliders, toggle switches, icons, progress bars, and more. This was just the first part; there were second and third parts...

All you need is a bit of time and attention, but it's clear that only a middle school reading comprehension level is required.

Next time you open any software's "settings dialog" (whether on mobile, desktop, or web), you'll notice they're all composed of the same basic elements, just arranged or colored differently — because they share the same design language. Each element's meaning is highly standardized, and you'll understand them instantly.

You might be amazed at how long you've been held back by this knowledge gap. Isn't it amusing? And now, don't you feel incredibly liberated, incredibly savvy, incredibly lucky? "Thank goodness I'm no longer bound by that tiny knowledge gap!" — That's a fact of incredible liberation.

Enhance your "tool methodology": whenever you get new software, check the "settings dialog" early. You've mastered a "foreign language"— the "Graphical User Interface Design Language."

Once again, outpacing 99% of people isn't that hard. It's just about checking the settings dialog... Not only is it simple, but it's also surprisingly effective.

[^1]: If one day you decide to start learning programming, there's one language you absolutely shouldn’t miss: LISP—short for *LISt Processor*. This language was specifically designed for artificial intelligence research, and “LIST” is the *only* data structure in this language.

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