Why RAG Matters: The "Reference Book" Strategy

If you ask a standard GPT model about your private company's budget, it will lie. It hasn't been trained on your data. RAG (Retrieval-Augmented Generation) is the technique of giving the model the data during the prompt.

1. The RAG Workflow

Retrieve: Find relevant facts in your Vector Store (Module 6).
Augment: Insert those facts into a Prompt Template (Module 3).
Generate: Ask the LLM to answer the question only using those facts.

2. RAG vs. Fine-Tuning

Fine-Tuning: Like a doctor going to medical school for 10 years (Slow, expensive, fixed knowledge).
RAG: Like a doctor doing a surgery with an open textbook (Fast, cheap, always up to date).

3. Visualizing the RAG Loop

graph TD
    User[Query: 'What is my PTO?'] --> V[Vector Store]
    V -->|Search| C[Context: 'You have 15 days']
    C --> P[Prompt: 'Answer using this Context: {C} User: {User}']
    P --> LLM[Chat Model]
    LLM --> Result['You have 15 days of PTO.']

4. The "Grounding" Instruction

The most important part of a RAG prompt is the Constraint: "You are an expert assistant. Answer the user's question using the provided context. If the answer is not in the context, say 'I do not know'. Do not use your own internal knowledge."

This instruction is what turns a creative LLM into a reliable Truth Machine.

5. Engineering Tip: Citation Support

Because you have the metadata from Module 5, your RAG system should always include source links.

Output: "Our vacation policy is 15 days (Source: HR_Handbook.pdf, Page 12)."

Key Takeaways

RAG grounds AI responses in external, verifiable facts.
It is cheaper and more flexible than fine-tuning.
The "Grounding Instruction" prevents the AI from being creative with facts.
Citations build user trust by showing the "Source of Truth."

Module 7 Lesson 1: Why RAG Matters