My understanding of the big model: the magic of language
In-depth exploration of the operating mechanism and limitations of AI big models, revealing the truth behind language illusions.
Core content:
1. The essence of big models processing information: form rather than meaning
2. The essence of prompts: guiding AI rather than communicating with it
3. AI output: the most likely words to be said, not the truth
4. AI reliability issues: logic seems real, but it actually imitates humans
5. The quality of human-computer interaction depends on the ability to ask questions
6. AI's "right to speak" and bias issues
7. AI's impact on human expression, thinking and judgment habits
1. Large Model
Commonly used AI, or large model products, whether it is ChatGPT, DeepSeek or others, these large models do not really understand the information , but only learn how to reproduce specific expressions in a large number of languages.
It deals with form, not meaning; it is good at prediction, not understanding. The big model forms an ability to "look like it understands" through countless language associations.
The so-called "intelligence" is more about the cleverness of language representation rather than thinking at the conscious level.
2. Prompt
Prompt is not asking questions to AI, but setting how it should answer.
The "communication" between humans and AI is not the interaction between two conscious entities , but rather we use natural language to construct context, guide it into a specific context, and allow it to automatically complete the response within the track we set.
An effective prompt is to confine AI to a controllable thinking framework.
3. Capabilities of Large Models
The answer given by AI is based on the fact that it has " seen enough " and is "subconsciously recreated", making people mistakenly believe that it "understands":
But it has no idea what it is saying; it is simply reproducing what a human might say in a similar context.
What it outputs is not "truth" but "what is most likely to be said."
4. The problem of large models
Coherent language can easily win trust, but trust does not mean reliability.
AI often makes mistakes without realizing it is wrong. After all, for it, there is no "right" or "wrong", only "which statement is most likely in this context".
Its logic may sound real, but it's just imitating the way you speak.
The more certain you are, the more plausible it will seem.
5. Human-computer interaction
The performance of AI depends largely on the questions asked.
The better you get at building prompts, the more it behaves like an expert: it’s not that the AI is smart, it’s that you’re thinking for it.
What we train is not just the model, but also how to ask questions, how to guide, and how to set goals.
6. Behind the Technology
AI is a tool, but its "right to speak" lies in the hands of those who can manipulate the input.
The model itself is not biased, but it learns from reality, and reality itself is biased.
The AI's output may seem neutral, but it is actually just a default.
The so-called "default" is often what is defined by mainstream, convention and power.
