GEO & AI Search

RAG (Retrieval Augmented Generation)

Retrieval Augmented Generation is a method where an AI model retrieves relevant external documents at query time and uses them to ground its generated answer, rather than relying only on its training data.

It is why fresh, well-structured, publicly accessible content can appear in AI answers even if the model was trained before that content existed. For example, an assistant answering a current question may fetch and cite live web pages. Being retrievable and quotable is the entire game.

Retrieval systems chunk pages before embedding them, usually by section, which makes document structure a technical concern rather than a stylistic one. A page organised under clear headings produces clean chunks that each cover one idea. A page written as continuous prose produces chunks that start and end mid-thought and retrieve poorly. Placing supporting facts near the claim they support matters for the same reason, since a statistic three sections away from the argument it backs may end up in a different chunk entirely. Server-rendered HTML is the other requirement, as most retrieval crawlers do not execute scripts.