Retrieval-Augmented Generation (RAG)

Definition

A technique where an AI model retrieves relevant external documents at query time and generates its answer from them.

RAG combines a search step with generation: the system fetches passages relevant to the question, then conditions the model's response on that retrieved text rather than relying solely on training data. This is the mechanism behind most cited AI answers, and it is why fresh, crawlable, well-structured pages can appear in responses even about very recent topics. Understanding RAG is central to GEO, because winning visibility means being the passage that gets retrieved and then faithfully quoted.