How models retrieve & rank
Retrieval-Augmented Generation (RAG)
Retrieval-augmented generation (RAG) is a technique where an AI model fetches relevant external documents at query time and uses them to ground its answer, instead of relying only on its training data. For AEO, RAG is why well-structured, crawlable content can be cited in answers the model would otherwise get wrong.
Why it matters for AEO
RAG is the mechanism that makes current, external content citable. Because retrieval works at the passage level, RAG rewards self-contained, directly responsive passages. If your content cannot be fetched or cleanly excerpted, the retrieval step skips it and your information never reaches the answer. Most major answer engines rely on some form of RAG, which is why crawlability and clear structure are foundational to being cited.
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