Why it matters to a buyer
RAG is why AI visibility can be improved on a timescale of weeks rather than waiting for the next model to be trained. If an assistant retrieves pages before answering, then a page that is crawlable, current and clearly written can enter its answers soon after it is indexed. For a buyer, it also explains why the same question gets different answers in different assistants: each one retrieves from a different index, so each needs to be checked separately.
How it is measured or applied
For a site, applying RAG means making pages easy to retrieve and easy to quote. The retrieval step needs the assistant's crawler to be allowed and the page to be in the index the assistant searches. The generation step favours passages that answer a question in a few self-contained sentences with specific facts. Measurement looks at retrieval directly where possible, for example server logs showing visits from AI crawlers, and at outcomes, meaning how often each assistant cites the page for a tracked set of prompts.
Sources
- Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks, arXiv (Lewis et al.)
