Retrieval-Augmented Generation (RAG)
Retrieval-Augmented Generation (RAG) is an architecture in which a system first retrieves documents relevant to a query, then feeds them to a language model to generate the answer. RAG powers most AI search engines, which is why the pages that get retrieved — yours or a third party's — largely determine what AI says about you.
RAG splits the answer pipeline into two stages with different failure modes. Retrieval decides which pages the model sees: if your canonical pricing page is not retrieved, its facts cannot appear in the answer no matter how well written it is. Generation decides how those pages are synthesized: even good sources can be compressed or blended imperfectly.
GEO maps directly onto the two stages. Winning retrieval is about being findable and selectable — topical relevance, crawlability, extractable structure, source authority. Winning generation is about being unambiguous once selected — clear standalone claims that survive compression without distortion.
Understanding RAG also explains engine differences: each engine's retrieval index, ranking, and source preferences differ, so the same model family can produce different brand answers behind different retrieval stacks. Diagnosing a bad answer starts with the question 'what did it retrieve?' — usually visible in the citations.
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