Retrieval-Augmented Generation (RAG)
Also called: RAG
Retrieval-augmented generation is a technique where an AI system searches for relevant documents at query time and uses their contents to generate its answer, rather than relying only on training data.
RAG is why AEO is tractable at all. If answers came purely from training, influencing them would mean waiting for retraining. Because a retrieval step fetches live pages, changes to a website can affect answers within days.
Retrieval is also where most of the leverage sits. Whether your page is fetched, and whether the passage is extractable, are far more controllable than what a model absorbed months ago.
Why it matters
Understanding RAG explains both the opportunity and the timeline — and separates work that can move quickly from work that cannot move at all on a useful horizon.