# RAG (Retrieval-Augmented Generation), Explained

You've watched an LLM confidently invent a function that doesn't exist, cite a policy your company never wrote, or shrug at a question it should have known the answer to. The model isn't broken - it just doesn't *have* your data. It only knows what it absorbed during training: a frozen, generic snapshot of the public internet, with nothing about your codebase, your docs, or last Tuesday's incident.

RAG is the standard fix. The idea is calmer than the acronym suggests: before you ask the model anything, you go and *fetch the relevant facts* from your own data, then hand them to the model and say "answer using these." This guide builds that idea up properly - what problem it solves, the exact pipeline that makes it work, and the real reasons it's harder to do well than the diagrams suggest.

## How to read this

- **Just need the gist of what RAG is?** Read [Phase 1: The Problem RAG Solves](01-the-problem-rag-solves.md) - it gives you the whole mental model in one sitting.
- **Want it to actually make sense?** Read in order. Phase 1 is the *why*, Phase 2 is the *how*, and Phase 3 is the *why it's harder than it looks* - the part that separates a demo from something you'd trust in production.

## The phases

1. **[The Problem RAG Solves](01-the-problem-rag-solves.md)** - why an LLM alone makes things up about your data, and the open-book-exam mental model that fixes it.
2. **[How RAG Works](02-how-rag-works.md)** - the pipeline: chunk your docs, embed them into a vector store, retrieve the most relevant chunks at query time, stuff them into the prompt, and generate.
3. **[Why It's Harder Than It Looks](03-why-its-harder-than-it-looks.md)** - RAG quality is retrieval quality. Bad chunking, ignored context, stale indexes, thin retrieval, and the clear line between RAG and fine-tuning.

> Deliberately deferred: the deep mechanics of *how* embeddings and vector search work live in their own guide, [Embeddings and Vector Search](/guides/embeddings-and-vector-search). This guide uses them as a building block and links there when you want to go deeper.