RAG: Giving the AI a "Reference Book"

As we learned in Module 2, AI models are "Stateless." They only know what was in their training data. If you ask about your company's private policy, the AI will hallucinate.

RAG (Retrieval-Augmented Generation) is the technique of giving the AI a "Reference Book" to read from before it answers.

1. The RAG Problem

Prompt: "What is my company's vacation policy?"
Vanilla AI: Hallucinates "15 days" because that's common in the training data.
RAG AI: Searches your company's PDF $\rightarrow$ Finds "25 days" $\rightarrow$ Answers correctly.

2. The 3 Steps of RAG

Retrieve: Find the relevant paragraph in your private files.
Augment: Insert that paragraph into the prompt context.
Generate: Ask the AI to answer only using that paragraph.

3. Visualizing RAG

graph TD
    User[User Question] --> S[Search: Vector Database]
    S -->|Find| P[Private Fact: 25 days PTO]
    P --> App[System Prompt: 'Answer using this FACT: ...']
    App --> LLM[AI Model]
    LLM --> Final['Your policy is 25 days.']

4. Why RAG is the #1 Business Use Case

Companies don't need AI to write poetry; they need AI to answer questions about their Internal Documentation, Legal Contracts, and Customer Support logs. RAG makes this possible without retraining the model.

💡 Guidance for Learners

RAG is like an "Open Book Exam." The AI doesn't need to memorize the facts; it just needs to be good at Reading and Summarizing the facts you give it.

Summary

RAG solves the problem of hallucinations by providing external evidence.
It allows AI to work with private, real-time data.
The system first searches for data, then summarizes it for the user.
RAG is the foundation of almost all corporate AI chatbots.

Module 5 Lesson 1: Retrieval-Augmented Generation (RAG)