How AI Picks What to Cite

When an AI model answers a question, it draws on patterns from its training data and, increasingly, on live web pages it retrieves in real time. This live-lookup process is called retrieval-augmented generation, or RAG.

Three things make a page more likely to get picked up. First, clear, direct answers near the top of the page, not buried after 800 words of introduction. Second, structured data that establishes what the page is and who runs it - Organization and Article schema are the ones most consistently tied to AI tools correctly identifying a source. FAQ and HowTo schema can still help machines parse "this is a question, this is its answer," though neither earns a rich result in Google Search anymore (Google retired HowTo’s in 2023 and FAQ’s in May 2026) - treat them as a parsing aid, not a visibility lever. Third, established authority signals such as E-E-A-T (Experience, Expertise, Authoritativeness, Trustworthiness), real author bios and citations to credible sources.

If a human would have to scroll and skim to find your answer, so will a machine, and it will likely move on to a competitor’s page that made it easier.