What is query fan-out?

Query fan-out is the step where an AI search system breaks one question into a set of smaller searches, runs them in parallel, and assembles its answer from whatever those searches return. A buyer asks which option suits their situation. The engine quietly searches the category definition, the leading options, the price drivers, the common objections and the recent coverage, then writes a paragraph that reads as though it answered the original question directly.

The searches are invisible to the person asking and invisible in your analytics. You see neither the sub-queries nor the ranking contest each one ran. What you may see, if you are watching server logs, is an assistant crawler fetching several pages of your site in quick succession, which is a fan-out arriving rather than a person browsing.

Fan-out is why AI answers feel more thorough than a search result and why they are harder to influence. A traditional result page rewarded one page for matching one query. An answer built from ten sub-queries can pull ten different sources, and a brand named in that answer is usually named because it appeared in several of them rather than because it won the headline query.

The mechanism sits inside the retrieval stage of the pipeline every answer engine shares. Our explainer on the RAG pipeline covers the stages in order, and how AI engines choose which sources to cite covers what happens to the pages once they have been fetched.

How many sub-queries does one prompt actually generate?

One prompt generates roughly nine to eleven sub-queries on the measurements published so far, with a long tail well above that. Seer Interactive's research on Gemini 3 fan-outs ran 501 tracked prompts through the Gemini 3 API in 2026 and recorded an average of 10.7 fan-out queries per prompt.

Nectiv Digital's analysis of more than 60,000 captured Google fan-out queries put the average at 9.06 sub-queries per prompt, and reported that 59% of prompts triggered between 5 and 11 searches while 24% triggered between 12 and 19. Its industry breakdown found software the highest fan-out category at 11.7 sub-queries per prompt and local the lowest at 3.79, so how much fan-out you face depends on how considered and comparison-heavy your market is.

Study	Scale	Average sub-queries	Notable detail
Seer Interactive, 2026	501 tracked prompts, Gemini 3 API	10.7 per prompt	95% of the fan-out queries had no global search volume
Nectiv Digital, 2026	60,000+ captured Google fan-outs	9.06 per prompt	59% of prompts triggered 5 to 11 searches, software highest at 11.7

Read those averages as orders of magnitude rather than constants. Fan-out width varies with how open the question is, how commercial it is and how much the engine already holds about the subject, and every measurement so far comes from a vendor or agency dataset rather than from the engines themselves. What all of them agree on is the shape: one prompt, many searches, and a citation contest you never see run.

Why does query fan-out break keyword research?

Query fan-out breaks keyword research because the searches that decide the answer are searches nobody types. Seer Interactive's 2026 study reported that 95% of the fan-out queries it captured had no measurable global search volume, which means they are absent from every keyword tool, invisible to every rank tracker, and missing from any content plan built by sorting a spreadsheet by monthly searches.

A second break follows from the first. Position tracking loses its meaning when the query being ranked for is generated on the fly and never repeats in exactly that wording. You can still track whether you are named and cited, which is why AI visibility measurement moved to prompt sampling, but the familiar "we rank fourth for this term" sentence has no equivalent inside a fan-out.

The useful replacement is to plan around questions rather than terms. A buyer's real decision generates a predictable cluster: what is this, which options exist, what does it cost, what goes wrong, how do I check the claim, who is it wrong for. Those are the shapes a fan-out reliably produces, and you can enumerate them from your own sales conversations without any tool at all.

Keyword volume still has a job, just a smaller one. It tells you which framings are common enough to be worth using as the visible title, which is a classic search decision. It no longer tells you what to cover, because coverage is now judged against a set of questions the engine invents at query time. Our note on what actually differs between AEO and SEO works through where the two disciplines still agree.

How do you write a page that wins more than one sub-query?

Writing for fan-out means covering the adjacent questions on the same page, each under a heading worded the way a buyer would ask it, each answered outright in its first sentence. A page that answers only the question in its title is eligible for roughly one slot in ten. A page that also answers the comparison, the cost drivers, the failure modes and the verification question can be retrieved for several sub-queries at once.

Keep every section able to stand alone, because an engine lifting your answer to sub-query seven never read sections one through six. Restate the subject in the opening sentence instead of writing "it" or "this", keep sections to a few hundred words so they are quotable whole, and avoid making a section depend on a table three screens earlier.

Answer before you explain. The sentence immediately under a heading is the extractable unit, and a warm-up paragraph puts your answer where a model will not find it. Where two or more options exist, put them in a table, since a table row survives extraction better than the same comparison written as prose.

Then give each claim something to stand on. Name the source of every number in the same paragraph as the number, link it where you can, and date it. A model checking whether your page supports the sentence it is about to attribute to you is doing a shallow, mechanical check, and a figure with a named source beside it passes that check while a bare number does not. Our AI citation checklist lists the page-level items in order of impact.

How do you find the fan-out queries for your own topic?

Finding your own fan-out queries is an observation exercise, since no keyword tool holds them. Start by running your real buyer questions through each engine several times and recording every source cited in every answer. The citation list is a fingerprint of what was searched: a page about pricing appearing in the answer to a "which vendor" question tells you a pricing sub-query ran.

Read the free public signals alongside that. The People Also Ask box and the related searches strip for your query are Google's own view of the neighbouring questions, and they are close relatives of the sub-queries a fan-out produces. Both are free, both are visible without a tool, and both are ignored by most content plans.

Watch your server logs for the assistant crawlers. A burst of fetches across several unrelated pages in the same second is a fan-out touching your site, and the set of URLs fetched tells you which of your pages the engine considered relevant to a single question. That is as close to a direct reading of the mechanism as most businesses can get.

Finish with the cheapest source of all, which is a salesperson. Anyone who runs discovery calls can list the six questions that always follow the first one, in the customer's own words, in about ten minutes. Those questions are the fan-out, written by the people who generate it, and turning each one into a heading on the relevant page is the most direct way to be eligible more than once in the same answer.

What else do people ask about query fan-out?