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Query Fan-Out

Query fan-out is a technique in which an AI search system splits one question into several related sub-queries, runs them at the same time across subtopics and data sources, and combines the results into one answer. Google describes it for AI Mode and AI Overviews; it explains why a page can be cited for a question it never mentions.

  • Industry term

By QueryRev ResearchPublished Updated5 min read

Why it matters.

Google named the technique when it introduced AI Mode in March 2025: issuing multiple related searches concurrently across subtopics and data sources, the Knowledge Graph and shopping data among them, then bringing the results together in one response[1]. At I/O in May 2025 it described AI Mode breaking a question into subtopics and issuing a multitude of queries on the user’s behalf, and Deep Search issuing hundreds[2].

For a site, the unit of competition shrinks. A question about which bed frame suits a small bedroom and a heavy mattress becomes searches about frame sizes, weight limits, slat spacing and storage. Each sub-query has its own results, and the answer can link the best page for each, so a page that answers one part well can earn a place without ranking for the question as asked. Google says fan-out lets AI Overviews and AI Mode show a wider and more diverse set of links than a classic search[3].

It also explains part of why AI answers vary between runs. When the plan of sub-queries changes, so does the pool of pages an answer draws from, and a page cited on Monday can be missing on Tuesday without anything on it having changed.

Google’s AI features read pages through Googlebot. Other AI search systems that gather several sources for one answer read the web with their own crawlers, and few sites close the door to all of them: in QueryRev’s study, only 43 of the 6,294 sites read, 0.7%, block every one of the AI crawlers covered by name[6]. For most questions, access rarely decides who is in the pool; coverage of each subtopic is what separates the pages an answer uses.

How it is measured.

Google does not publish the sub-queries behind an answer. Search Console counts AI Mode impressions and clicks under its standard rules and treats a follow-up question as a new query with data of its own[4], so fan-out shows there only indirectly, as longer and more specific queries for which a page earns impressions.

Bing shows more. The AI Performance report in Bing Webmaster Tools lists grounding queries, the key phrases its AI used when retrieving content that was cited in answers, beside citation counts per page[5]. It is the closest first-party view of fan-out a site owner has, and it covers Copilot and Bing’s AI summaries, not Google.

For the rest, model it. Break each tracked prompt into the subtopics a complete answer needs, map each to the page that should answer it, and record which pages the answers cite. The gaps are subtopics with no page, or with a page whose answer is buried.

Measured by

  • Subtopics per prompt
  • Pages cited per subtopic
  • Grounding queries (Bing)

A worked example.

Sample data: an invented site, to show the method.

A bed retailer tracks one question: which bed frame suits a small bedroom and a heavy memory foam mattress. Broken into the sub-queries an answer needs, it covers frame sizes, slat spacing, weight limits and storage. The retailer has strong pages for the first two.

The answer cites its size guide and its slat guide, a competitor’s page for weight limits, and nothing of the retailer’s on storage. The work is two passages in pages the retailer already has, not a new page for the whole question.

One question, four sub-queries

Sample data. One question split into four sub-queries: the retailer’s pages answer two and are cited, a competitor is cited for one, and one has no page on the site.

What to do, step by step.

  1. For each tracked prompt, write down the subtopics a complete answer needs: the questions behind the question.

  2. Map each subtopic to the page that should answer it, and mark the ones with no page or a buried answer.

  3. Answer each subtopic in its own passage, under a heading that names it, so it can be retrieved without the rest of the page.

  4. Link the pages of a topic to each other, so a system that finds one can find the others.

  5. Read which pages the answers cite over several runs and compare them with the map: a subtopic cited to a competitor is the next passage to write.

  6. Where Bing Webmaster Tools shows grounding queries, add them to the map as observed sub-queries.

Mistakes to avoid.

  • Optimising only for the question as typed. The answer is assembled from the sub-queries. A page tuned to the full question and silent on its parts loses to pages that answer the parts.

  • A new page for every sub-query. Thin pages for each phrase scatter the topic. A passage in the page that owns the topic usually serves a sub-query better than a new URL.

  • Mapping the sub-queries once. The plan behind an answer can change with the model and the wording. Read the citations again over time instead of trusting the first map.

  • Reading fan-out as a list of keywords. Repeating related phrases answers none of them. Each subtopic needs its answer stated plainly.

How QueryRev treats it.

QueryRev ties each tracked prompt to the topics and queries it concerns and keeps the pages every answer cites, so a subtopic cited to a competitor shows as a gap on a specific page. The fix it proposes is a passage, with the prompts it serves and its evidence, as an Earn citation decision that a person approves.

Questions people ask.

Is query fan-out only a Google technique?

Google names it for AI Mode and AI Overviews. Other AI search systems also retrieve several sources for one answer, and Bing reports the grounding queries its AI used, but each vendor describes its own method, so read their documentation rather than assuming one process.

Can I see the fan-out queries for my site?

Not in Google Search Console, which reports AI Mode with the rest of Web search and without the sub-queries. Bing Webmaster Tools shows grounding queries for answers that cited your pages.

Does query fan-out make long-tail keywords more important?

It makes the subtopics of a question important, each answered clearly. Those often match long-tail queries, but the aim is a passage that answers one part well, not a page for every phrase.

Sources and citation.

Primary sources only: the search engines, the AI vendors, standards bodies and QueryRev’s own research. Each is cited in the text by its number.

  1. Expanding AI Overviews and introducing AI ModeGoogle ·

  2. AI Mode in Google Search: Updates from Google I/O 2025Google ·

  3. AI features and your websiteGoogle Search Central ·

  4. What are impressions, position, and clicks?Google Search Console Help ·

  5. Introducing AI Performance in Bing Webmaster Tools Public PreviewMicrosoft Bing ·

  6. State of AI crawler access, October 2026QueryRev Research ·

Cite this definition

A suggested citation. Its link goes to the definition, whose anchor does not change.

QueryRev Research. “Query Fan-Out.” QueryRev AI glossary, 18 October 2026. https://queryrev.com/glossary/query-fan-out#definition

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