Retrieval-Augmented Generation (RAG)

Introduction

RAG is a game-changing architecture that combines the reasoning capabilities of LLMs with your own private, up-to-date data. It solves the problems of hallucinations and outdated model knowledge.

How RAG Works

Think of RAG like an open-book exam. Instead of answering from memory, the AI looks up the facts first.

1. Indexing: Your documents (PDFs, docs, codebase) are split into chunks, converted into embeddings, and stored in a vector database.
2. Retrieval: When a user asks a question, the query is converted into an embedding. The vector database retrieves the most semantically similar document chunks.
3. Generation: The retrieved chunks are injected into the LLM prompt alongside the user's question as "context". The LLM then generates a factual answer based exclusively on that provided context.

Assignment

Knowledge check

• What are the three main steps of the RAG pipeline?
• How does RAG help reduce AI hallucinations?