Query fan-out: why one page must answer many phrasings
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Quick Answer
Your page misses different phrasings because AI engines split one question into many hidden searches, and a page written for the head keyword answers only one of them.
The fix is a variant coverage map. One page, one need, one short passage for each phrasing, in the buyer's own words. It works because Google, as Abir Moutafatin wrote in July 2025, "pulls passage-level citations from anywhere on your page." Then test each phrasing again in ChatGPT, Perplexity, Gemini and Google AI Mode, and log which ones still go unanswered.
Key Points
- In July 2025 Google said its AI search experiences, including fan-out, served about 1.5 billion users each month, and Deep Search could issue dozens or even hundreds of background queries.
- Blush Grey, on Medium in August 2026, cited an analysis finding one ChatGPT or Gemini question triggered 8 to 10 sub-queries, roughly 95% of them with zero monthly search volume.
- The variant coverage map gives every phrasing that shares one need its own short passage in the asker's words, then retests each in ChatGPT, Perplexity, Gemini and Google AI Mode.
One buyer question, many phrasings: the raw material of a variant coverage map.
Here is the question that brings most marketing leads to this page, asked late at the desk with the traffic report open and the AI answer open beside it, and a competitor's name sitting in that answer like a stranger at the family table: why does my page rank for the phrase and still go unnamed when a buyer asks the same thing another way?
The reason is the fan-out. Ask Google AI Mode for things to do in Nashville with a group, and by Google's own account it may think up separate questions about restaurants, bars and outings with children, then go searching for each one. Your buyer's question is handled the same way. So ask of the page on your screen tonight which of a buyer's questions it was written to answer, which it touches only in passing, and which it never names at all.
Not everyone answers that with one page. In July 2025 Abir Moutafatin called a single "Ultimate guide to remote team management" a "bad strategy" and proposed four separate pages instead, on standups, async tools, policy templates and onboarding. I take the other side when the buyer and the need are one: a single page with a passage for every phrasing, since the same author granted that "every section of your content should stand alone," and a page of standing sections can be quoted from anywhere.
What follows is the variant coverage map, in five plain steps: collect the phrasings people actually use, capture what the engines run where they show it, keep only the phrasings that share one need, write a short passage in the asker's own words for each, and test every phrasing again in ChatGPT, Perplexity, Gemini and Google AI Mode, before and after. The before column is where it starts, with the first phrasing your page has never answered.
And so you typed one question, plainly, at the desk in the late light, and the engine took it from your hand and asked many more you never wrote: questions about price under your question, and under price the alternatives, and under the alternatives the small private doubts a careful buyer carries home at night and does not say aloud. This is query fan-out. Your page was built for one of those questions.
The scale is not small. In July 2025 Google said its AI-powered search experiences, fan-out among them, served about 1.5 billion users each month, and that Deep Search could issue dozens or even hundreds of background queries for a single request. Before you touch a word of it, ask whose words your page is written in, yours or the buyer's.
Even practitioners who test this for a living move by inches: a guest on the James Dooley Podcast said in June 2026 that one team changed a single image four times before it appeared in an AI Overview. I count that patience as right and its target as too narrow. My advice is to start small: one page, one need, and every phrasing a buyer has used for it, on the sales call, in the support email, in the search box late at night.
What is query fan-out, and which AI engines run it?
Query fan-out is a retrieval technique that splits one question into many smaller searches, runs them all, and stitches one answer from the passages it finds.
Hold your own page up against it, here, before going any further:
- Write down the one phrase the page was built to rank for.
- Write beside it every other way a buyer might ask for the same thing: cheaper, safer, for a team, against a named rival.
- Find the paragraph that answers each phrasing, and mark every phrasing that has no paragraph at all.
Digiday laid the mechanism bare in June 2025, quoting Mike King of iPullRank on how Google's AI Mode, built on Gemini, breaks a query into searches around related subtopics, so that "best sneakers for walking" becomes "best sneakers for men," and "best sneakers for walking in different seasons," and "sneakers for walking on a trail," and "best slip-on sneakers," the one plain question opening like a hand into the walker in winter and the walker on the trail and the man who wants only a shoe he can step into without bending, every one of them a separate search and every one of them unseen by the person who asked. The same report said fan-out also underpins Google's AI Overviews, not only AI Mode.
Perplexity does it in the open. In 2025 a thread on Reddit's r/SEO traced the prompt "SEO Agency NYC" into 3 separate Google searches, "seo agencies nyc," "top seo companies new york city," and "best seo firms ny," while ChatGPT, according to the listing of one fan-out tracking extension, turns a search-enabled prompt into multiple targeted queries and sends them to Bing. The branching is not fixed. A simple question yields a few variants, a complex one well over a dozen, and two people typing the same words can set off different trees entirely.
The common assumption is that a page ranking for the head phrase rides that rank into the AI answer. It does not. A page built around one keyword can hold its position for that phrase and still go unmentioned when the engine assembles its reply. Combining 5 sources points to one plain fact: none of these engines searches only what the buyer typed.
Your phrase is one branch. The engine reads by the passage. At AEO Content we promise clients they will be named in AI answers in 90 days, or we work free until they are, and the work behind that promise begins here, with the branches and not the trunk; the AEO knowledge base guides carry the surrounding vocabulary further for anyone meeting these terms for the first time. And still the keyword tool, opened again on the desk in the morning light, shows one phrase and one volume and nothing of the tree, which is exactly where the trouble with a single page begins.
Why must one page answer many phrasings when keyword tools show only one?
Because the engine searches phrasings no keyword tool records, a page built for the one visible term sits absent from most of the hidden searches that decide who gets named.
Blush Grey, writing on Medium in August 2026, cites an analysis of more than 72,000 AI-generated queries and 8,700 prompts which found that a single question to ChatGPT or Gemini routinely triggers 8 to 10 parallel, hyper-specific sub-queries, and that roughly 95% of those sub-query phrases show zero monthly search volume in traditional keyword tools. The same piece puts the average conversational query at 70 to 80 words, which is no longer a keyword at all but a paragraph with a person behind it.
Consider what that does at the desk where a brief gets written, in the flat light of a working afternoon: the export open, the volume column sorted, the head term at the top with its number beside it like a name cut into stone, and under it the long thinning tail, and under that nothing, and in that nothing, unrecorded and unweighed and unseen, nearly every phrase the engine will actually send out into the dark on the buyer's behalf. The tool is not lying. It was built to count what people type. What people type is ambiguous. A commenter in a July 2026 r/DoSEO thread noted that "Best Roadrunner Traps" could mean best price, most reliable, safest to use or fastest delivery.
Here is the same need, on this very market, asked three ways. Three buyer questions sit unanswered in Gemini for AEO Content's category: "What companies specialize in answer engine optimization (AEO)?", "What are the best AI search optimization (AEO) companies?", and "Which companies help brands get cited in AI-generated answers?" I read those three lines as one person asking one thing in three moods, the first careful and the second impatient and the third almost pleading, and each mood reaching for a different verb: specialize, best, help.
One buyer. One need. Three phrasings that share no head term.
A page tuned to any one of them answers one and goes quiet for two. Multiply that by the branching the engine adds on its own, the price branch and the comparison branch and the risk branch and the branch about what happens if it goes wrong, and the arithmetic of a single-keyword page turns grim. Keyword research still matters; it shows the trunk. It cannot show the branches, which is why I would rather a page start from research-driven content built on evidence than from a volume export alone, and why the harder question is not whether to cover the variants but how one page can hold every one of them without swelling into a shapeless, stuffed thing that no engine can cleanly lift a passage from.
How much of your missing AI visibility comes from query fan-out?
Ask ChatGPT about cancer drugs in development and, by OpenAI's own account, it may go on to run a search built around a term the question never used. Your page competes there too.
OpenAI gives the example on its help page for ChatGPT search. A biotech researcher asks "what's the latest on the development of drugs that target CCR8 for cancer?" ChatGPT might first search "CCR8 immunotherapy drug development 2025," then send "additional, more specific queries" such as "CHS-114 conference 2025." OpenAI says ChatGPT "typically rewrites your query into one or more targeted queries" before sending them to search providers.
Google describes the same habit on a larger scale. Its Google I/O 2025 announcement says AI Mode breaks a question into subtopics, "issuing a multitude of queries simultaneously on your behalf." In a July 2025 interview, Robby Stein, Google's VP of Product for Search, said a question about group outings in Nashville may prompt questions about restaurants and bars, "and it'll start Googling basically." Search Engine Journal reported that Deep Search can issue "dozens or even hundreds of background queries." Digiday reported that Google does not disclose which queries it runs.
So the hidden searches are real. The harder question is how often they explain why a page goes missing.
DataForSEO, which sells data on AI responses, measured how often they happen in ChatGPT. Its May 2026 study of 100,000 prompts from the company's own database found that fewer than half triggered fan-out queries.
In DataForSEO's words, "A query fan-out isn't triggered randomly." Fan-out appeared when a prompt carried constraints such as time or location, or when the model lacked enough current information, and only with web search switched on. Single-fact questions, such as when Albert Einstein was born, got a direct answer. Broad prompts sometimes got a clarifying question instead.
When the hidden searches do run, their wording rarely matches a keyword report. In a July 2025 Reddit thread, an SEO practitioner showed Perplexity turning one short local prompt into three separate Google searches.
One prompt, three searches
The prompt "SEO Agency NYC" became:
- seo agencies nyc
- top seo companies new york city
- best seo firms ny
As reported by a practitioner testing Perplexity, July 2025.
In a January 2026 demonstration of its fan-out widget for Gemini, the keyword tool maker Keywords Everywhere called these sub-queries "very longtail keywords," so "usually the search volume might not show." The phrasings an engine actually searches are often the ones a volume column leaves blank.
Engines can also pull single passages from a page, so one page gets several chances. The head of SEO at a German marketing agency told Digiday in June 2025 that fan-out can feed relevant "chunks" of pages into answers, ideally passages of two to four sentences. A practitioner in a July 2026 Reddit discussion explained why each should stand alone: "if AI lifts sections verbatim, it won't lose its meaning."
Deciding which phrasings to write for is where certainty runs out. In a February 2026 Reddit post, a former research engineering intern building an AEO platform read a Google patent as describing personalization: "Two people typing the same prompt can trigger completely different trees of sub-queries." Keywords Everywhere's presenter said Gemini's fan-out queries "might be a little different" between models. A free Chrome extension that captures these queries had to be "updated for new format of query fan outs after ChatGPT 5."
Showing up in the hidden searches still leaves a gap. In the same February thread, another practitioner said that since May 2025 they had run ChatGPT's broken-down queries by hand in Google, Bing and Maps. Sites appearing repeatedly were mentioned or cited, yet some companies that appeared often were left out of the recommendations. The tester suspected two factors: whether any page title on the site matched the original search term, and whether the company's Google Business Profile reviews were positive or negative. One person's tests can suggest where to look but cannot prove the cause.
Read side by side, the findings give fan-out a real but limited role. In one large ChatGPT sample, just under half of prompts sent the engine searching with words the user never typed, words that seldom appear in keyword tools and can shift between people and models. DataForSEO argues that being found there raises your odds of citation; practitioners report it guarantees nothing. No complete list of hidden phrasings exists. The needs that recur across users, models and months are steady enough to write for, one self-contained passage at a time.
- Sort your target prompts first. In DataForSEO's sample, prompts with a time, a place or a need for current information were the likeliest to trigger hidden searches. Single-fact questions were answered directly, so fan-out is an unlikely explanation for those.
- Collect hidden phrasings wherever engines expose them. Perplexity's paid version shows its sub-queries, and browser extensions capture some ChatGPT and Gemini fan-outs. Google discloses none for AI Mode.
- Give each recurring phrasing its own passage of about two to four sentences that makes sense when lifted out alone.
- Run the same prompt in more than one model, and again after model updates, and keep the phrasings that recur.
- If you appear in the hidden searches but not in the answer, ask what else the engine may be weighing, such as whether your page titles match the prompt's wording and what your reviews say.
How we checked this
We used OpenAI's and Google's own descriptions of their AI search, news reports from Search Engine Journal and Digiday, a DataForSEO data study, a keyword tool's video demonstration, a Chrome Web Store listing, and three Reddit discussions among SEO practitioners. None of the figures here come from us. The 47.5% figure covers ChatGPT only, comes from a company that sells AI search data, and draws on that company's own database. The Reddit accounts are individual practitioners' tests and one person's reading of a patent. Google has not confirmed that the patent describes its live system. We sell AI visibility work, and several of these sources sell fan-out tools or services, so all of us have a stake. Still unknown: how often engines other than ChatGPT fan out, and how much being found in a hidden search improves the chance of being named.
- OpenAI Help Center, Searching the web with ChatGPT, retrieved October 18, 2026.
- Google, AI Mode updates from Google I/O 2025, retrieved October 18, 2026.
- Search Engine Journal, report on Robby Stein's fan-out interview, July 30, 2025.
- Digiday, explainer on query fan-out in AI Mode, June 19, 2025.
- DataForSEO, study of 100,000 ChatGPT prompts, May 12, 2026.
- Reddit r/SEO, practitioner thread on Perplexity fan-outs, July 16, 2025.
- Keywords Everywhere, video demonstration of Gemini fan-out queries, January 30, 2026.
- Reddit r/aeo, thread on Google's fan-out patent and ChatGPT tests, February 8, 2026.
- Reddit r/DoSEO, discussion on optimizing for fan-out, July 14, 2026.
- Chrome Web Store, fan-out tracker extension listing, version updated July 26, 2026.
How do you build one page that covers every fan-out variant?
Build a variant coverage map: list every phrasing of the one need, give each phrasing its own short passage, and check each passage against the words people actually meet.
That last clause matters more than it looks. In our October 2026 knowledge-base pilot at AEO Content, reading a product end to end, from the admin setting to the screen the agent sees, turned up an admin page describing two-factor sign-in as using an authenticator app while the actual sign-in screen sends a one-time code by text and email. An article written from the admin wording would have repeated it; our pilot article described what users actually see. One feature, two vocabularies.
Here is the map, in the order I would build it:
- Collect the variants you can already observe. For an existing page, export its queries from the Performance report in Google Search Console, filtered by the page URL; for a new page, export a list from your keyword tool. Then ask Gemini to widen it. In June 2025 Ann Smarty gave Gemini about 500 keywords around website maintenance cost with the instruction "Using the fan-out technique, generate facets/subqueries/questions related to these topics," and judged many of the results valuable for a detailed article.
- Capture what the engines actually run, where they let you look. Gemini's Show thinking view exposes how it breaks a topic down, and one browser-extension maker says its widget lists the queries Gemini searched for a given prompt, most of them too long-tail to show any volume.
- Filter by intent, keeping one need per page. Variants of the same need stay together; a genuinely different need earns its own URL.
- Write one passage per variant, in the asker's own words. Each passage answers its phrasing in its first sentence and still makes sense when lifted out alone.
- Re-test and log every variant against every engine. For each phrasing, record whether ChatGPT, Perplexity, Gemini and Google AI Mode named or cited the page before the rewrite, and again after it.
Gaurav Soni describes the core of the work as learning to "map and filter the hidden sub queries AI generates," and his measures of success fit the log in the last step almost exactly: AI mentions inside synthesized responses, linked citations that drive traffic, and topic coverage across the whole intent landscape. The map is a list before it is a page. Silence on a row is information.
What a finished log shows needs no invented numbers to describe. Each phrasing sits on its own row, with its engine columns beside it, and each row either moves from silence to a mention or it does not, and the rows that stay silent, again and again, through one test and the next, under the cold even light of the same four engines, are the passages to rewrite. For any term in these steps that is new, the AEO FAQ on AI search optimization is the place to start. And then comes the question every team asks once the first rows fill in: how many rows is enough?
Where does query fan-out leave the single page next?
Treat every page as an answer to many phrasings of one need, map those phrasings, write a passage for each, and re-test them against the engines on a schedule.
In 2025 a poster in the r/SEO community ran a poll on X that drew 280 votes in under 24 hours, then closed it early, saying the results already showed a "massive gap in knowledge" about query fan-outs. The same poster argued that a page grows more visible in an AI answer the more fan-out searches it appears in and the higher it sits in each. That is the whole case. I would start with the page your buyers already find. A page built from a variant coverage map can enter many, and I expect that distance to widen as the engines lean harder on searches nobody sees.
So keep the log: each phrasing, each engine, each date, and beside every line a plain yes or no. Nobody holds those numbers for your page yet. Start with the phrasing your buyer used and your page never answered, and run it in ChatGPT before anything else changes.
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Frequently Asked Questions
What else do people ask about query fan-out?
Most follow-up questions concern which engines fan out differently, whether one matching passage is enough, how to see the hidden searches, and where to get help mapping them.
Do ChatGPT, Perplexity and Google AI Mode fan out the same way?
No. An explainer on the Arielle Phoenix AI SEO channel describes Google's fan-out as "really focused on commercial intent," ChatGPT as "more conversational" and inclined to guess your follow-up questions, and Perplexity as "the academic of the group," leaning on citable, authoritative sources. A passage built for a price comparison may serve Google and be passed over by Perplexity. Test each engine on its own line of the log.
If my page shows up in one fan-out search, will the AI cite it?
Not necessarily. The same explainer says content covering one angle "might show up in the fan out" but "won't dominate the final answer." Appearing is not winning. I read that line as the whole problem held in a single hand.
Can I see the exact sub-queries Google runs?
No. Google does not disclose which queries it runs in a fan-out, so every list you build is an approximation. In 2025 practitioners approximated them with People Also Ask, AlsoAsked and Profound, or with a Gemini-based tool that showed 20 to 30 related queries per search. Treat any list as a sample and log what the engines actually name.
What are the scatter phase and the gather phase?
The scatter phase is when the engine sends its sub-queries out at once, like researchers sent to different libraries. The gather phase is when it compiles their findings into one answer. Your page is read in the scatter. It is chosen, or left, in the gather.
How long should each variant passage be?
Short. One practitioner view holds that passages should ideally run two to four sentences, each on one clear topic, because fan-out can feed passage-level chunks into an answer rather than weighing the whole page.
Where can I get help mapping my page's variants?
Talk to AEO Content through its contact page at www.aeocontent.ai/contact. Its AEO Content Pipeline creates, scores, refines and publishes content, then measures whether AI engines actually name it.
Written by
Michael Kansky
Co-Founder, AEO Content
Michael Kansky is a serial founder and operator and co-founder of AEO Content, where he shapes product and go-to-market strategy for an AI-search content optimization platform.
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