Retrieval-Augmented Generation (RAG)

RAG Overview

RAG helps AI answer using information retrieved from documents instead of relying only on memory.

You just learned how AI can sound confident yet be wrong. RAG is one of the biggest fixes for that, it grounds answers in real documents before the AI responds.

This is a simplified learning demo. Real RAG systems may use more advanced steps.

Interactive Playground

Your Question Knowledge Base

Live Visualization

💬 User Question

Waiting for question…

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🔍 Search Documents

Not searched yet

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📄 Retrieved Context

No context retrieved yet

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🤖 Grounded Answer

Run RAG to see an answer…

Statistics

Documents

Retrieved

Best Match

Answer Source

Grounding

How It Works

💬

Question

→

🔍

Search
Documents

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📄

Retrieve
Context

→

🤖

Send to
AI

→

✅

Grounded
Answer

🎓 Tips 3 tips

Retrieval comes first. RAG searches your documents before the AI ever writes an answer, so the response is based on real text, not guesses.

Try editing the knowledge base text or asking something unrelated, if nothing matches, a good RAG system says so instead of making something up.

This demo uses simple keyword matching. Real RAG systems typically use vector search over embeddings for more nuanced matches.

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Key Takeaway

RAG makes AI answers more reliable by giving the model useful information before it responds.

Next: Document Chunking & Overlap