Query fan-out is the technique AI search systems use to split one question into several related searches and combine the results. Google confirms AI Overviews and AI Mode use it. A 2025 study shows how many searches one prompt can trigger, and that most of them are invisible to keyword tools.
- Google’s documentation says AI Overviews and AI Mode “may use a ‘query fan-out’ technique,” issuing multiple related searches across subtopics and data sources.
- In Seer Interactive’s November 2025 test of 501 prompts, Gemini 3 averaged 10.7 fan-out queries per prompt, and 95% had zero reported monthly search volume.
- About 26.4% of those fan-out queries named a brand, so being known for a category affects whether you are even searched for.
- The study forced search on every prompt, so it shows the mechanism, not how often real users trigger it.
What Google says query fan-out does
Query fan-out: a retrieval technique in which an AI system turns one user question into multiple related searches across subtopics and data sources, then assembles a single answer from the combined results.
Google’s own Search documentation on AI features states that both AI Overviews and AI Mode “may use a ‘query fan-out’ technique,” and that this helps its models identify more supporting web pages. Google adds that these features can show a wider and more diverse set of links than a classic search results page.
The same page is explicit about eligibility. A page must be indexed and eligible to appear in Search with a snippet, and Google says there are no additional technical requirements or special optimizations for AI features beyond standard SEO best practices. For a buyer, that is a useful anchor: fan-out changes which questions your pages must answer, not the technical rules for being included.
What the Seer Interactive study measured
Seer Interactive’s R&D lead, Nick Haigler, published an initial analysis on November 21, 2025. It ran 501 prompts that Seer tracks for itself and clients through the Gemini 3 API, across several industries, and recorded every search Gemini issued while answering.
Gemini 3 averaged 10.7 fan-out queries per prompt, in a range from 3 to 28. That was up 78% from an average of 6.01 for Gemini 2.5. The queries averaged 6.7 words, and 21.3% contained a year, mostly 2024 or 2025. Only about 1% of fan-out queries appeared more than once across the whole dataset, so almost every query was unique.
One caveat matters before you act on any of this. Seer forced Google Search grounding on every prompt, so the study does not measure how often Gemini searches on its own. Treat the figures as a picture of the mechanism, from one vendor and one model, not as a rate for AI Mode in general.
Why keyword tools cannot see it
The study’s most useful finding for planning is that 95% of fan-out queries had zero global monthly search volume. Keyword tools build volume figures from what people type into search engines. A machine-generated query such as a specific comparison of two vendors on one attribute was never typed by anyone, so it has no volume to report.
That has a direct consequence for how marketing teams prioritize topics. A volume-first keyword plan will systematically miss the long-tail questions an AI system asks on a buyer’s behalf. It will also overstate the value of a single head term when the answer is assembled from a dozen narrower lookups.
| Observation | Figure | Source and scope |
|---|---|---|
| Average fan-out queries per prompt | 10.7 (range 3 to 28) | Seer Interactive, Nov 2025, Gemini 3, 501 prompts |
| Fan-out queries with zero monthly search volume | 95% | Seer Interactive, Nov 2025 |
| Fan-out queries naming a brand | 26.4% | Seer Interactive, Nov 2025 |
| Fan-out queries containing a year | 21.3% | Seer Interactive, Nov 2025 |
| Fan-out queries shared across the dataset | About 1% | Seer Interactive, Nov 2025 |
What it changes about content planning
If one buyer question becomes about ten searches, then a page that answers only the headline question competes for one of ten slots. Haigler observed that fan-outs tend to expand a prompt into comparisons, attributes and deeper details, not simple rewordings. Pages that cover those angles clearly are the ones available to be retrieved.
Brand names add a second effect. In the Seer data, 26.4% of fan-out queries included a brand, and the author noted that in one payroll-software example four of sixteen fan-outs named a brand that then appeared in the answer. If systems already associate a vendor with a category, they may search for it directly. That fits the pattern in our review of brand mentions versus backlinks, although neither dataset proves cause.
The year pattern is a hypothesis only. Haigler suggests that years in fan-outs may be recency cues. Keeping dates and facts current on comparison pages costs little, and our piece on what AI search means for SEO covers the wider context.
A practical order of work
Start with the questions your buyers ask, because those are what an AI system will decompose. The steps below avoid guessing at hidden queries.
- List five to ten real buying questions from sales calls and support tickets, phrased as a buyer would phrase them to an assistant.
- For each, write out the comparisons, attributes, pricing, integrations and risks a careful researcher would check, as a stand-in for the likely sub-searches.
- Check whether you have a clear, indexable page answering each sub-question, and fill the gaps with specific, dated, sourced content.
- Run the original questions through AI Mode and ChatGPT and note which sources appear, following our guide to measuring AI search visibility.
- Repeat quarterly, since the number and type of sub-queries differ by model and change as models change.
Frequently Asked Questions
Can I see the fan-out queries Google generates for my topic?
Not directly in Google’s own reporting. Researchers like Seer Interactive capture them by calling model APIs with search grounding enabled, which approximates but does not replicate what consumer AI Mode does.
Does query fan-out replace traditional SEO?
No. Google’s documentation says pages need to be indexed and eligible for snippets, with no extra technical requirements. Fan-out raises the value of topical coverage on top of that foundation.
Is 10.7 queries per prompt a number I can plan around?
Only as an indication. It comes from one model, 501 prompts and forced search. Real behavior will vary by question type, product and model version.
Mapping a buyer’s real questions to the sub-searches behind them is research work that pays off across both classic and AI search. At Grow with Hafsa we help teams build AI-ready content and search visibility systems, and we are glad to review your current coverage with you.
Fan-out queries often surface forum threads alongside publisher pages. Our analysis of Reddit and AI citations looks at which community content assistants actually cite.