RAG

Overview

Retrieval-Augmented Generation combines search with LLMs, letting AI access external knowledge to provide accurate, up-to-date responses.

What is it?

An architecture that retrieves relevant context before generating responses.

Why it matters

RAG solves LLM limitations: outdated training data, hallucinations, and lack of private knowledge. It's the most deployed AI pattern.

How it works

1) Query comes in, 2) Embed and search for relevant documents, 3) Add retrieved context to prompt, 4) LLM generates answer grounded in the context.

Real-World Examples

Tools & Libraries