📚 RAG Explained Like You're 5
Building AI systems and writing about how they actually work. Master of AI @ University of Technology Sydney. Previously B.Tech CS with focus on IoT. I believe the best way to learn is to explain. That's why I'm documenting tech concepts with simple analogies (@sreekarreddy.com). AWS Certified • Azure AI Certified • Neo4j Professional • Google Data Analytics When not coding: exploring Sydney, working on side projects, and teaching tech to anyone who'll listen.
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:
- Question comes in → "What's our refund policy?"
- Retrieval → Search your documents for relevant info
- Augment → Add that info to the prompt
- 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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