Understanding Retrieval-Augmented Generation

The Problem with Pure LLMs

Large language models are frozen in time the moment training ends, and they can confidently produce information that sounds right but isn't.

How RAG Works

Retrieval-Augmented Generation combines a search step with a generation step: relevant documents are retrieved from a knowledge base and passed to the model as context before it writes an answer.

Why It Matters

RAG reduces hallucination, keeps answers current, and lets you cite sources — all without retraining the underlying model.

Getting Started

A minimal RAG pipeline needs just three pieces: a vector store, an embedding model, and a language model to synthesize the final response.