Let's talk about RAG in words that ordinary people can understand

What is RAG?

Imagine you ask a top student a question, and he looks up information in a book before answering you - this is RAG (Retrieval Augmented Generation). This AI technology allows the language model to "look up information" before answering, and combine the latest information to give a more reliable answer.

1. Why do we need RAG?

Traditional AI models are like students memorizing textbooks:

• Knowledge stays at the time when the "textbook" was published (training data cutoff)
• When encountering a problem that you don't know how to solve, you tend to make up something (produce hallucinations)
• Lack of knowledge in specialized fields (such as medical law)
• Answers are always textbook and don't take into account the latest events

RAG installed an "intelligent search engine" for AI:

• ✅ Automatically check the latest information before answering
• ✅ Cite real sources
• ✅ Professional questions can be checked in professional documents
• ✅ "Learn and apply" like humans

2. RAG work in three steps

1. "Information search phase"

• Convert your problem into "mathematical code" (vectorization)
• Find relevant content in the Knowledge Base (Company Documents/Latest News/Professional Materials)
• "[Similar to using keywords to find books in a library]"

• 「Combination Information Pack」

• Package the information you find with your questions
• "[It's equivalent to giving the AI ​​a hint: "Please use this information to answer this question"]"

• Generate answers

• AI combines its own knowledge + provided information to write answers
• "Like a writer creating a story based on material"

3. Real Application Scenarios

1. 「Smart Customer Service」 : Check the latest product manual to answer users’ questions

   ❝

   "How long is my router's warranty period?" → Automatically check warranty policy

   ❞

2. 「Medical consultation」 : Combined with the latest medical guidelines

   ❝

   "How long is the interval between booster shots of the COVID-19 vaccine?" → Quoting the latest notice from the National Health Commission

   ❞

3. 「Financial Analysis」 : Integrate real-time market data

   ❝

   "Why did Tesla's stock fall today?" → Analysis of the latest financial report + industry news

   ❞

4. "Legal consultation" : related legal provisions and cases

   ❝

   "What should I do if the rental deposit is not refunded?" → Quoting Article 710 of the Civil Code

   ❞

4. How is it better than traditional AI?

Comparison Items	General AI	RAG Enhanced AI
Knowledge Update	Stay in training	Stay updated
Answer basis	From memory	There is a real source
Professional issues	Error-prone	Check professional documents
Preventing fabrication	Possibly fabricated	Data constraints
Customization	General answer	Access to corporate knowledge base

5. Summary: Simplified version of technical principles

1. "Build a knowledge base"

• Digitize company documents/product manuals, etc.
• "Like a library putting labels on every book"

• 「Smart Search」

• Understand the essence of the problem (not just keyword matching)
• 「【Example: "I want to return or exchange a product" → Automatically link to the "Return Policy" section】」

• "Combination Creation"

• Not a simple copy and paste
• "[Organize meeting minutes like a secretary: extract the key points and reorganize them using natural language]"


#RAG