📚 RAG Explained Like You're 5

An open-book exam for AI

Day 3 of 149

👉 Full deep-dive with code examples

The Open-Book Exam

Imagine two types of exams:

Closed-book exam:

• You mostly rely on what's in your head
• If you didn't memorize it, you're stuck

Open-book exam:

• You can look things up
• Reference your notes
• Find the specific answer

RAG gives AI an open-book exam!

The Problem

LLMs (like ChatGPT) have knowledge frozen at training time:

• Don't know recent events
• Can't access your private documents
• Sometimes make things up (hallucinate)

How RAG Works

Retrieval-Augmented Generation:

1. Question comes in → "What's our refund policy?"
2. Retrieval → Search your documents for relevant info
3. Augment → Add that info to the prompt
4. Generate → AI answers using the found documents

User: "What's the refund policy?"
     ↓
[Search company docs]
     ↓
Found: "Refunds within 30 days with receipt..."
     ↓
AI: "Based on your policy, refunds are allowed within 30 days..."

Why It's Powerful

• ✅ AI can answer about YOUR data
• ✅ Answers are grounded in real documents
• ✅ Reduces hallucinations
• ✅ Can stay up-to-date (as your docs change, answers can change)

In One Sentence

RAG lets AI look up information before answering, like having notes during a test.

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