PART 1 · MODULE 4Retrieval-Augmented Generation

RAG

How AI gets smarter by looking things up

The problem: AI can make things up

LLMs are trained on data up to a cutoff date. They don't know about recent events, your company's documents, or private data. When asked about these, they might hallucinate — confidently generate incorrect information.

The solution: give AI a "cheat sheet"

RAG (Retrieval-Augmented Generation) solves this by first searching a knowledge base for relevant information, then passing that information to the LLM along with your question. The AI answers based on real data, not just memory.

How RAG Works

Your Question

Knowledge Base

Accurate Answer

LLM + Context

Relevant Docs

Try it yourself

Upload a .pdf,.docx, or.txt file, then ask questions about it. Everything runs in your browser — no data leaves your machine.

RAG PlaygroundRxDB

1. RxDB Ready2. Upload3. Chunk + Index4. Query + Retrieve

Drop .pdf, .docx, .txt, or .md files here

Files are chunked, vectorized, and stored in RxDB — 100% in your browser

Real-world analogy

✕Without RAG

"What's our company's return policy?"

→ AI guesses based on general knowledge. Might be wrong.

With RAG

"What's our company's return policy?"

→ AI reads your actual policy document first, then answers accurately.

💡 Think of it like this

An LLM without RAG is like a new hire answering from memory on day one. An LLM with RAG is like that same hire with full access to the company wiki, Confluence, and shared drive. Same brain, much better answers.

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Embeddings Multimodal LLMs