AI tools are now everywhere. For someone completely new to them, the first reaction is often: where should I start? This chapter first explains what a large language model (LLM) is, and then discusses how to use it.
What Do We Mean by AI?
If you’ve been listening to friends chat recently, eight out of ten sentences might be about “AI.” But did you know? The AI everyone is talking about now is not the same as the AI from a few years ago.
The AI that’s currently taking the internet by storm has a professional name: Generative AI.
What does that mean? Simply put: You give it a sentence, and it can generate an article for you; you give it an idea, and it can generate an image; you give it a requirement, and it can write a piece of code.
It’s like an incredibly smart assistant. You say “write a resignation letter for me,” and it actually writes one; you say “draw a cat wearing a suit,” and it actually draws one. This ability to “create content from nothing” is the core of “generative.”
The ones we often hear about—ChatGPT, Claude, DeepSeek—all belong to this category. Their essence is a Large Language Model (LLM), which learns to understand and generate human language by training on massive amounts of text data.
But here’s an important secret:
AI doesn’t actually “understand” what you’re saying; it’s just “calculating probabilities.”
This might sound counterintuitive, but let me explain:
When you ask AI “What color is the sky?”, it doesn’t actually look up at the sky, nor does it truly understand the concepts of “sky” and “color.” Here’s how it works:
Based on the hundreds of millions of articles it has read, when the preceding text is “What color is the sky”, the probability that the next word is “blue” is 82%, “gray” is 10%, “red” is 5%… and then it picks the word with the highest probability.
Here’s a more vivid example:
You ask: “What is 1+1?”
- AI isn’t “doing math”; it finds that in all the text it has seen, “1+1 equals” is followed by “2” 99.9% of the time.
- So it answers “2.”
You ask: “Recommend a good movie.”
- AI hasn’t actually watched movies; it analyzes that in the texts it has learned, “good movies” are often followed by words like “The Shawshank Redemption,” “Titanic,” etc.
- It recommends the ones with high probability.
It’s like a super overachiever who has read all the books in the world, but:
- He can tell you “Paris is the capital of France” (because the books say so).
- But he has never been to Paris, and doesn’t even know what “capital” means.
- He just remembers that when you say “Paris is,” the most frequent continuation is “the capital of France.”
So AI can have some strange issues:
- Sometimes it “confidently talks nonsense” (fabricates non-existent facts).
- It might get simple math problems wrong (because it’s not really calculating).
- It may give contradictory answers (inconsistent probability calculations).
This phenomenon of “confidently talking nonsense” has a technical term: Hallucination. Just like humans dreaming, AI “dreams” up things that don’t exist, and says them with great confidence.
How to Solve Hallucination with Reasoning
Since AI is so prone to errors, what can we do? Scientists came up with a solution: Let AI “think” before answering.
This technique is called “Thinking” or “Chain of Thought.”
How traditional AI works:
- You ask a question → AI immediately spits out an answer.
- Like a student who sees a problem and blurts out the answer without working it out on scratch paper.
AI with reasoning:
- You ask a question → AI first calculates and thinks on “scratch paper” → then gives you the answer.
- Like a student who first lists steps, checks them, and then writes the final answer.
A practical example:
You ask: “Xiao Ming has 15 apples. He gives 3 to Xiao Hong and then buys 8 more. How many does he have now?”
Traditional AI might:
- See the numbers “15, 3, 8.”
- Based on probability, think they should be added.
- Answer: “26” (wrong!)
AI with reasoning would:
- First analyze: “Xiao Ming initially has 15.”
- “Gives 3 to Xiao Hong, so 15-3=12.”
- “Buys 8 more, so 12+8=20.”
- Finally answer: “20” (correct!)
Benefits of reasoning:
- Reduces hallucinations - Because AI “checks” its own thoughts, it’s less likely to talk nonsense.
- More rigorous logic - Derives step by step, without skipping steps.
- Explainable - You can see how AI thinks (though most of the time this thinking process is hidden).
- Handles complex problems - Accuracy improves significantly for problems requiring multi-step reasoning.
But reasoning also has costs:
- Slower speed (because it needs to “think”).
- Higher cost (more computation).
- Not all problems need reasoning (e.g., “hello” doesn’t need half a day of thinking).
Now most AIs have added this reasoning ability. For example, ChatGPT’s thinking model, DeepSeek’s deep thinking mode.
A quick summary:
- Regular AI = a student who quickly blurts out answers (may be wrong).
- Reasoning AI = a student who carefully works on scratch paper (higher accuracy).
This doesn’t mean AI can truly “think”; it just adds a few verification steps before probability calculation. But the effect is clear—fewer hallucinations, more reliable answers.
To use the most fitting analogy: Generative AI is like a “super liberal arts student” who has read countless books and seen the world. You can ask it anything, and it can chat with you and help you write things. However, it answers by “memorizing the question bank” rather than truly understanding knowledge. The new versions are starting to learn to “work on scratch paper.”
Actually, the AI Family Is Huge
Many people think AI is just ChatGPT, but it’s not. The AI family has many members:
1. Recognition AI - The one that sees clearly
- Facial recognition (phone unlock, face payment)
- Image recognition (identifying flowers by photo, recognizing text)
- Speech recognition (Siri, Xiao Ai understanding your speech)
- These AIs specialize in “recognizing,” not “creating.”
2. Recommendation AI - The one that knows your mind best
- Douyin (TikTok) recommends videos to you.
- Taobao recommends products.
- NetEase Cloud Music recommends songs.
- They excel at “guessing what you like,” but don’t generate new content.
3. Decision AI - The one that’s great at chess and games
- AlphaGo (playing Go)
- Game AI (like the computer opponent in StarCraft)
- They focus on “making decisions,” surpassing humans in specific domains.
4. Generative AI - The current hot wave
- Text generation: ChatGPT, Claude, DeepSeek
- Image generation: Midjourney, Stable Diffusion, DALL-E
- Video generation: Sora, Runway
- Music generation: Suno, Udio
Summary
A quick summary: If we compare AI to a toolbox, recognition AI is a “magnifying glass,” recommendation AI is a “compass,” decision AI is a “chess manual,” and generative AI is a “magic wand”—it can conjure up what you want.
But this “magic wand” is actually a “probability calculator.” It’s powerful, but not omnipotent. And this tutorial is mainly about how to use this “magic wand” well.