1. Agent: The "Worker" in AI

Imagine that you have a virtual employee who is on call 24 hours a day and can observe the environment, think about countermeasures, and use tools to complete tasks. This is an intelligent agent.

• The difference from traditional AI : Traditional AI is like a machine and can only execute commands according to a fixed process; while Agent is more like a real person and can flexibly respond to complex situations. For example, it can automatically decompose tasks according to user needs (such as booking a flight → checking the weather → recommending an itinerary), and can also call databases and computing tools.
• Core abilities : good memory (long-term memory + short-term memory), ability to make plans, and ability to use tools (such as looking up information and sending emails).

2. LLM (Large Language Model) - AI's "student brain"

LLM is a large model like ChatGPT, which is essentially a super language master.

• Ability : Learn basic language rules through "nine years of compulsory education" (massive text pre-training), and then master specific skills through "professional training" (fine-tuning), such as writing code, translation, and poetry.
• Disadvantages : If a top student has not reviewed a certain subject for an exam, he will guess blindly (when encountering knowledge outside the training data, he tends to "talk nonsense", which is called hallucination in the industry ).

3. RAG (Retrieval Enhanced Generation) - Give AI a "Portable Database"

In order to solve the "illusion" problem of LLM, RAG is equivalent to installing an external hard drive for AI, which is filled with the proprietary knowledge of enterprises or individuals.

• How to use : Before answering each question, check the database (such as product manuals, internal documents), and then feed the relevant content to LLM for reference.
• Benefits : Reduces the probability of AI making up stories, and can also mark the source of citations like a paper. For example, when customer service AI encounters unpopular questions, it can quickly check the company's latest policies before answering.

4. Prompt Word Engineering - The "Art of Speaking" for Communicating with AI

If you want AI to do its job well, the key is to be able to "give instructions".

• Bad example : “Write me a travel guide” (too vague) → The AI ​​will probably write a bunch of nonsense.
• The correct approach : "Generate a 3-day Beijing itinerary, including the Forbidden City and the Great Wall, with a daily budget of 500 yuan, and recommend an old Beijing restaurant with an average price of 50 yuan per person, with the address and signature dishes included."
• The secret : clarify the task (what to do), the specific object (what to do), provide reference examples, and set format requirements.

Summary: How do they work together?

Imagine you want to develop a "travel planning AI":

1. LLM is the brain, responsible for understanding user needs and generating text;
2. RAG provides the latest data on attractions and restaurants to avoid recommending closed stores;
3. The agent coordinates the entire process: first checking the weather, then calling the map API to calculate the route, and finally using LLM to generate the itinerary;
4. The prompt words are the detailed requirements document you give to AI to ensure that the output meets expectations.