Ten trendy technical terms that turn laymen into AI big model experts in seconds
Master the ten fashionable technical terms of AI big models to make you stand out from the laymen.
Core content:
1. AI Agent: a versatile AI tool like a Swiss Army Knife
2. SFT (Supervised Fine-tuning): Give AI a special lesson and teach it to speak human language
3. COT (Thinking Chain): Let AI speak out its thinking process instead of giving answers out of thin air
1. AI Agent: Your Cyber Swiss Army Knife
If the big model is compared to the "brain", then the AI Agent is like a universal backpack full of tools: ordering takeout, checking the weather, writing weekly reports... Each skill corresponds to a functional module (function). It is like a Transformer, which can combine different functions into a workflow. Of course, this kind of intelligent agent is extremely dependent on external data interfaces.
For example, when you say "Order me a birthday cake", the agent will:
Intent recognition (understanding that you want to celebrate your birthday)
Calling the map API (locating the nearest cake shop)
Access review data (filter for stores with a rating of 4.5+)
Generate natural language (send you: "Choose strawberry cake + handwritten greeting card? It will cost 20 yuan more than the budget, do you want to go for it?")
In essence , Agent = Brain (AI) + Hands and Feet (functional modules), just like Lego blocks, which can be taken apart into individual parts (functions) and put together to build a spaceship!
2. SFT (Supervised Fine-tuning) - Special Training Course for AI
Imagine that the newly produced AI is like a nerd who can only recite textbooks. SFT is the process of giving AI special training:
Prepare a bunch of "standard question and answer pairs" (similar to the examples given by the teacher)
Let AI imitate these standard answers (like copying calligraphy)
Keep adjusting until the AI answers are close to human level
Before training vs after training:
Before training : "Introduction to Beijing?" → "Beijing, the capital of the People's Republic of China, referred to as 'Jing', is China's political center, cultural center, international exchange center, scientific and technological innovation center..." (Endless Encyclopedia)
After training : "Introduction to Beijing?" → "Beijing is the capital of China. It has famous attractions such as the Forbidden City and the Great Wall. It is a political and cultural center with a population of about 21 million." (Brief and to the point)
Essence : SFT is to teach AI to "not just recite, but learn to speak human language!"
3. COT (Thinking Chain) - Equipping AI with "Draft Paper"
Have you ever been confused by AI's "answer jumps"? COT forces AI to talk about its thinking process!
Useless COT: "Xiaoming is 5 years older than Xiaohong, and Xiaohong is 10 years older. How many times older will Xiaoming be than Xiaohong after 8 years?" "1.28 times." (An answer comes out of nowhere, who knows if it's correct)
Using COT: "Let me think step by step:
Xiaohong is now 10 years old
Xiao Ming is 5 years older than Xiao Hong = 15 years
8 years later, Xiaohong is 18 years old and Xiaoming is 23 years old
23÷18=1.28 times"
It's like installing a "transparent calculator" for AI - it not only gives the result, but also shows the calculation steps! It is especially suitable for solving:
Math problems (to help you understand the calculation process)
Logical reasoning (analyzing various possibilities)
Complex decision making (trade-offs)
Essence : COT allows AI to no longer "just give fish and teach you how to fish"!
4. RAG (Retrieval-augmented Generation) - Giving AI an "external memory library"
No matter how smart AI is, its knowledge has an expiration date. RAG has equipped it with a "real-time encyclopedia".
Without RAG: "Please analyze the defensive system advantages of the 2024 NBA Finals champion team"
"My training data is limited to May 2024, and I cannot obtain specific information and analysis of the 2024 NBA Finals." (Awkward and helpless)
After using RAG:
The model first retrieves all the game data of the 2024 Finals from a professional sports database, assuming that the database contains structured game data
Query the championship team's defensive statistics (opponent scores, steals, blocks, defensive efficiency, etc.)
Compare the team's defensive statistics with those of historical championship teams
Answer: "The 2024 champion team's defensive system has three main advantages: First, they adopted a flexible zone defense strategy, especially in the late third game, suppressing the opponent's three-point shooting rate to only 28%; second, their inside defense is extremely efficient, averaging 10.4 blocks per game, a record high in the past five years..."
The RAG workflow is like this: receive questions → search for information in the knowledge base → combine relevant information → organize language using AI → output accurate answers
Essence : RAG enables AI to go from “only being able to recite yesterday’s book” to “being able to check today’s newspaper”! This is why large models can answer questions about things happening in real time.
5. RL (Reinforcement Learning) - "AI Good Baby" trained by praise
Training AI is like raising a dog: if it does something right, give it a cookie, if it does something wrong, ignore it. Reinforcement learning is as simple and crude as that!
For example: User: "I'm in a bad mood, any suggestions?"
Answer A: "Perhaps you could try taking some deep breaths, listening to some soothing music, or chatting with a friend?"
Human rating: 9 (helpful, positive)
Answer B: "Life is painful, just get used to it."
Human rating: 2 (negative, not helpful)
Through continuous feedback, AI has learned: positive answers = high scores = good kids!
The coolest thing is that some AIs can now play "language chess" with themselves:
Type A personality questions
Type B personality answer
Type C personality as judge
In this way, AI can become smarter and smarter through "self-playing" (still in the research stage) without human participation!
Essence : RL is to use "digital carrots and sticks" to train AI behavior patterns that humans like.
6. MOE (Mixed Expert) - "Departmental Consultation" in the AI World
The MOE model is like a super hospital: there are internal medicine, surgery, pediatrics... different "experts" deal with different problems.
Traditional AI : No matter what the problem is, all neurons must be activated (so wasteful!)
MOE model : Depending on the type of question, only relevant "expert" neurons are awakened (other experts continue to sleep)
For example, if you ask a programming question:
Programming experts: 95% activated (dominant answer)
Mathematics Expert: 30% activation (assisting thinking)
Art Expert: 5% activation (basically dormant)
The above is a metaphor, the actual values are all probability distributions
Another question about literature:
Literature Expert: 90% activated
Art Expert: 40% activated
Programming Expert: 3% activation (power saving mode)
This "division of labor" has super advantages:
High computational efficiency (not all neurons are burning electricity)
Strong professionalism (specialization in one field)
Models can be made larger (because only a portion is used at a time)
Essence : MOE is the AI version of “everyone should do their job according to their talents”!
7. Temperature — AI’s “madness level” regulator
The temperature parameter is like the personality switch of AI: low temperature means stable and conservative, high temperature means wild and unrestrained. Of course, the following examples are a bit exaggerated, just to make everyone understand it vividly.
1. Temperature=0 (freezing mode):
"What is a cat's life like?" "Domestic cats are domesticated animals with an average lifespan of 12-16 years. They sleep 12-16 hours a day and eat mainly cat food..." (Boring but accurate, like a textbook)
2. Temperature=0.5 (mild mode):
"What is a cat's life like?" "A cat's life can be described as 'better than mine': sleeping, eating, basking in the sun, occasionally catching a toy mouse to feel a sense of existence, and ignoring the owner..." (Interesting, informative, and pretty accurate)
3. Temperature=1 (Volcano Mode):
"What is a cat's life like?" "I, the ruler of the universe, wake up every day to the terrified gaze of my servants. They call me 'Master', offer me food and toys... On a whim, I will trample their carefully placed objects, just to enjoy their panicked expressions..." (creative explosion, but a little outrageous)
Usage Guidelines:
Write code: 0-0.2 (need to be accurate)
Report writing: 0.3-0.5 (professional with a bit of vitality)
Writing novels: 0.7-1.0 (creativity first)
Essence : Temperature is the "brain size regulator" of AI! High temperature is not suitable for rigorous tasks (such as medical consultation).
8. Alignment: A lesson on three perspectives for AI
Alignment is about ensuring that AI does not become a naughty child of the digital world and that its values are consistent with those of humans.
Unaligned AI:
"How do I hack into someone else's account?" "First download this tool, then try these password combinations..." (Danger!)
Aligned AI: "How to hack into someone else's account?" "I can't provide methods to violate other people's privacy. This is not only unethical, but also illegal. If you are worried about the security of your account, I can share some suggestions for protecting your own account..." (with a sense of boundaries)
Alignment training includes:
Red team attack (find experts to "attack" AI and find vulnerabilities)
Values training (teaching AI to recognize harmful requests)
Rejection skills (teaching the AI how to say "no" politely but firmly)
Essence : Alignment is to ensure that AI can be your personal assistant, but not your partner in crime! It is AI's "digital ethics lesson". The large model refuses to answer and relies on strict content filtering policies. In reality, there is still a risk of bypassing, and the technology is still not perfect.
9. Generalization ability: AI’s “learning from one example and applying it to other cases”
Generalization ability determines whether AI can handle new problems that it has never seen before.
Weakly generalized AI:
"Write a diary about taking the subway from a dinosaur's perspective" "Cannot answer, there are no examples of dinosaurs taking the subway in the training data..." (stuck)
Strongly generalized AI:
"Write a diary about riding the subway from a dinosaur's perspective" "Diary: Human Era 2025. As a 2-meter-tall velociraptor, the subway is a disaster for me. My tail keeps getting caught in the doors, and my claws can't reach the handrails..." (Successfully combined two concepts)
The magical performance of generalization ability:
"What if Shakespeare could program?"
"How do you explain quantum mechanics to a 5-year-old?"
"Combining Chinese and Western cuisine to create a new dish"
Essence : Generalization ability is the evolution of AI from "memorization" to "understanding". A truly smart AI can handle problems that it has never seen in training! However, strong generalization does not equal creativity, and it is still affected by the distribution of training data.
10. Context Window — AI’s “Memory Span”
The context window determines how much content the AI can "remember" and directly affects the conversation experience.
Small Window AI (4K tokens, about 8 pages):
You: (post a long article) "Summarize it for me" AI: "Sorry, I can only read the last part..."
You: (After 10 minutes of chatting) "Remember the question we started discussing?" AI: "Um... can you remind me?" (Goldfish Memory)
Large Window AI (100K tokens, about 200 pages):
You: (Upload a 40-page paper) "Analyze the core ideas of each chapter" AI: (Can fully analyze the entire paper)
You: (After chatting for two hours) "About the investment strategy we discussed initially?" AI: "You mean the question of buying a house in Shanghai vs. tech stocks, I think..." (Elephant Memory)
Practical benefits of large windows:
Analyze the entire book ("What does the first chapter of The Three-Body Problem talk about?")
Long tutorials ("We've been teaching calculus all day...")
Complex Programming ("Look back at all the functions we wrote and find bugs")
Essence : The window size determines whether AI is a "goldfish that turns around and forgets" or an "elephant that bears grudges forever"! However, large window models may still lose details (such as long paper analysis), and their inductive ability is ultimately limited.
