What it means
Query relevance is how well a page, or a single passage, matches what a search actually wants. In classic search, Google infers intent from the query and rewards content structured to match it. In AI search, relevance is scored as embedding similarity: The query and your text are turned into numeric vectors, and the closest vectors get retrieved and cited.
Why it matters
LLM prompts are longer and more specific than keywords, so relevance is judged against a hyperspecific query, often at the passage level. Query fan-out multiplies this: One prompt becomes many sub-queries, each needing a relevant passage. Pages that match many long-tail and question variations have higher citation probability in AI Overviews (AIOs) and chat answers.
Imagine a DTC skincare brand with one broad page on "best moisturizer." Against a conversational prompt like "fragrance-free moisturizer for eczema-prone skin in winter," that page scores low on embedding similarity and never gets retrieved. Break it into specific passages that each answer one variation, and the brand goes from 0 citations to appearing for 12 hyperspecific prompts.
How to use this knowledge
Write to specific intent instead of head keywords. Mine query refinements, autosuggest, and real customer language for the exact variations people ask. Structure content in passages that each answer one question, so each passage can be retrieved on its own. Match the format the query implies, the way Google rewards a step-by-step layout for "starting a business." Then rank in the top 10 for fan-out variations to raise retrieval and citation probability.
Growth Memo guidance
In classic Search, Google returns one ranked list for a query. In AI Mode, Gemini explodes your prompt into a swarm of sub-queries, each aimed at a different part of what you really care about. — Query fan-out
Since LLM prompts are conversational and varied, pages ranking for many long-tail and question-based variations have higher citation probability. — State of AI Search Optimization 2026
More data came out showing a high overlap between pages cited in AIOs and pages ranking in the top spots for the same query. — AI on Innovation
Search intent — the classic name for what query relevance measures: What the searcher is really after.
Embeddings — the vectors that let AI systems score relevance by meaning instead of keyword match.
Query fan-out — the reason relevance is now judged against many hyperspecific sub-queries per prompt.
Query refinements — the filter-style pills that reveal the specific variations users care about.
Topical authority — the site-level signal that complements passage-level relevance in earning citations.