Glossary
Retrieval-Augmented Generation (RAG)
Retrieval-augmented generation is an architecture that fetches relevant documents at query time and passes them to a language model, which then answers from that material. It was introduced in a 2020 research paper and is the mechanism behind AI answers that carry live citations to specific web pages.
In plain terms
The engine searches first and writes second. Your page has to survive the search step before the writing step can quote it.
Why it matters
RAG is why publishing still matters in an AI world. If your content is not retrievable at query time, no amount of brand strength puts it into the answer.
How to apply it
- Make pages individually retrievable: one topic, one URL, a descriptive title.
- Write self-contained sections that make sense when read out of context.
- Remove barriers to retrieval — gating, script-only rendering, blocked crawlers.
Example
A B2B insurance marketplace notices that Perplexity's answer to "what does E&O insurance cover for consultants" quotes its coverage-explainer page almost verbatim, including a citation link — a direct result of that page being retrieved at query time and handed to the model as source material, rather than the model recalling it from training.
Sources
Related terms
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Ranking is no longer enough
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