RAG (Retrieval-Augmented Generation)

Retrieval-augmented generation (RAG) is the technique that lets an AI assistant fetch real, current information — from the web or a set of documents — and use it to write its answer, instead of relying only on what it memorized during training.

Why it matters

RAG is why AI answers can cite live sources and stay reasonably current. It’s also the mechanism that decides which pages get pulled in and credited — so being clear, well-structured, and retrievable is what gets your content into RAG-powered answers. This is the technical heart of GEO.

In plain terms

Training gives a model its general knowledge; retrieval gives it today’s facts and the citations that come with them.