Query fan-out is the technique AI search systems use to turn one question into several related searches, run them in parallel, and combine the results into a single answer. Google describes it as "issuing multiple related searches across subtopics and data sources," and says it lets AI Overviews and AI Mode show a wider set of supporting links. The practical meaning for a marketer: you can be cited for a sub-question you never targeted, so your pages need to answer the specific follow-up questions around a topic, not just the head keyword.
Key Takeaways
- Google's own documentation confirms that AI Overviews and AI Mode may use query fan-out, and says no special optimisation is required beyond normal indexing and snippet eligibility (as of 14 October 2026).
- Vendor studies find fan-out queries are numerous (about 9 to 11 per prompt in several studies) and overwhelmingly have no measurable keyword volume. Treat them as themes, not keywords.
- Fan-outs are inconsistent between runs. Seer Interactive found only 8 queries appeared in all 13 runs of a tracked prompt set. Track topics, not exact strings.
- OpenAI does not publish how ChatGPT generates sub-queries, so any "ChatGPT fan-out recipe" is inference.
- The winning move is coverage: a topic hub plus focused sections or pages for the comparisons, criteria, prices, risks and proof that sub-queries ask about.
- Use the coverage map in this post to audit one topic in about an hour.
What query fan-out is, in plain terms
When you type a long, complicated question into an AI search tool, it rarely runs that exact text as a single search. It breaks the question into narrower searches, fetches results for each, and then writes an answer from what came back. Google's AI features documentation says AI Overviews and AI Mode "may use a 'query fan-out' technique" and that, as a result, they find more supporting pages and can display "a wider and more diverse set of helpful links" than classic search.
Google first described the mechanism publicly in its AI Mode announcement of 5 March 2025, saying it issues multiple related searches concurrently across subtopics and data sources and then combines the findings.
If you want a glossary entry, I cover the term in my AEO glossary, and the product itself in what Google AI Mode is.
What the studies actually show
Most of the numbers on fan-out come from tool vendors, so I'll attribute each one.
| Finding | Source | Notes |
|---|---|---|
| Gemini 3 averaged 10.7 fan-outs per prompt (range 3-28) across 501 prompts | Seer Interactive | Grounding was forced on every prompt, so it shows what fan-outs look like when they occur |
| 95% of those fan-outs had zero global search volume | Seer Interactive | Zero volume doesn't necessarily mean zero demand |
| 26.4% of fan-outs included a brand name; 21.3% included a year | Seer Interactive | Suggests brand and recency matter in retrieval; the author calls the brand inference reasonable, not tested |
| 47.5% of 100,000 ChatGPT prompts triggered fan-out; average word overlap with the prompt was 54% | DataForSEO | Vendor study using its own AI volume estimates; OpenAI does not describe the process publicly |
| Only 8 queries appeared in all 13 runs of tracked prompts (1,300 prompt runs, 8.52 average fan-outs) | Seer Interactive | One agency's tracked prompts, one model, one week |
| Ranking for more fan-out queries is associated with higher citation likelihood | Surfer SEO | Correlation from a vendor study; not independently verified |
Two lessons. First, fan-outs are plentiful but unstable: the same prompt produces different sub-queries on different runs. Second, nearly all of them are invisible to a keyword tool. That is why Ahrefs' query fan-out guide describes these as synthetic, inconsistent queries to prioritise by business fit rather than treat as keywords. Ahrefs also sells tools for this, so weigh its recommendations accordingly.
What platforms don't tell us
iPullRank's analysis notes that Google names the mechanism and describes it in patents, Microsoft's Copilot uses iterative retrieval, and OpenAI has not published details about its subquery generation. It also argues personalisation (location, history, device) can change the expansions for the same input, so visibility becomes a distribution rather than a fixed rank. That article is analytical, not a controlled study, and much of its mechanism detail comes from patents and research descriptions rather than observed behaviour.
The seven jobs fan-out queries tend to do
From the studies above and my own testing of AI answers, sub-queries fall into recognisable jobs. This taxonomy is my working model, not a platform specification. Ahrefs attributes a similar list of formats to Mike King's analysis of Google patents (related topics, implicit questions, comparisons, recency, reformulations, contextual variations, next-step queries).
- Define: what the thing is.
- Criteria: how to choose between options.
- Compare: X versus Y, alternatives.
- Quantify: price, duration, size, requirements.
- Prove: reviews, case studies, awards, data.
- Refresh: current-year, latest-version, "2026" variants.
- Act: how to start, apply, buy, book, or fix.
An illustrative worked example
The sub-queries below are my hypothetical illustration of how a prompt could be split. I did not capture them from a live system.
Prompt: "Which online data analytics course in India is good for a working professional with no coding background?"
| Job | Possible sub-question | Where your site should answer |
|---|---|---|
| Define | What does a data analyst do? | Short section on the hub page |
| Criteria | What to look for in a beginner-friendly course | Dedicated page or H2 with a checklist |
| Compare | Course A vs Course B vs self-study | Comparison page, honest about trade-offs |
| Quantify | Fees, weekly hours, duration | Program page with current figures and the date |
| Prove | Learner outcomes, curriculum review | Case studies; avoid unverifiable claims |
| Refresh | Best courses 2026 | Visible "last updated" and current facts |
| Act | How to apply, eligibility | Application steps near the top |
Notice that most of those sub-questions are answered by sections, not by separate keyword-targeted pages. The hub gets cited when it answers the whole set; individual sections get lifted when they stand alone.
A coverage map you can copy
Run this for one priority topic. It takes about an hour.
- Write the head prompt in the way a customer would phrase it to an AI tool.
- Run it in three places (Google AI Mode, ChatGPT with search, Perplexity or Gemini). Note the cited sources and any visible sub-questions or follow-ups. Run each prompt at least three times on different days, since results vary.
- List the themes you see across runs. Ignore one-off strings; keep themes that repeat.
- Map each theme to the seven jobs above.
- Score coverage per job: 0 = nothing on our site, 1 = mentioned, 2 = answered in a clear sentence, 3 = answered with evidence and a date.
- Fix the lowest scores first. Add the missing section, or a page if the sub-question deserves depth.
- Check third parties. If AI answers cite review sites, forums and comparison lists, your off-page presence matters too; my off-page AI visibility audit covers that.
| Score | Action |
|---|---|
| 0 | Create content; decide section vs page |
| 1 | Rewrite so the answer is in one self-contained sentence |
| 2 | Add proof: a number, source, example or date |
| 3 | Leave alone; recheck monthly |
What good coverage looks like on the page
Practices consistent with Google's guidance and with what the vendor studies recommend:
- Answer first. Put the direct answer under each heading before the nuance.
- Make sections self-contained. A passage should make sense if lifted out alone.
- Use headings that match real questions. Conductor's fan-out guide recommends mirroring likely sub-queries in headings and testing manually in public AI tools.
- State facts with dates. Seer found years appear in a fifth of fan-outs, which hints that freshness gets checked.
- Make brand and entity facts easy to find. Seer saw Gemini search for a company's awards and industries served, and fall back on third-party directories when the company had no dedicated page. Give those facts a home.
- Keep it indexable. Google says supporting links must be indexed and eligible to appear with a snippet.
What not to do
- Don't build a page per fan-out string. The Surfer article itself advises against chasing individual fan-out queries because they are messy and inconsistent. Doing so is also a route to thin pages; see my notes on scaled content abuse.
- Don't add "AI markup." Google says no special schema or AI files are needed.
- Don't report fan-out counts as a KPI. They vary by model, run and settings.
- Don't promise results. The link between covering fan-outs and being cited is correlational in the studies I read.
How to measure
Fan-out coverage is a means, not an outcome. Track whether you are cited and mentioned across a stable set of prompts, with multiple runs, and report a visibility percentage instead of a position. SparkToro and Gumshoe's January 2026 study, covered in my prompt research post, found list order is essentially random run to run but repeat presence is meaningful. In Search Console, see what the generative AI reporting shows, and keep a simple log of date, tool and result.
FAQ
What is query fan-out in simple terms?
It is when an AI search system splits one question into several narrower searches, runs them at once, and merges what it finds into one answer. Google documents that AI Overviews and AI Mode may use it, per its AI features guidance.
Can I see the fan-out queries for my topic?
Sometimes. Some AI tools show follow-ups or search steps, and vendors such as Ahrefs, Seer and DataForSEO extract them at scale. They vary by run, so look for repeating themes.
How many fan-out queries are there per prompt?
Studies report averages around 9 to 11 for several models, for example 10.7 for Gemini 3 in Seer's forced-grounding test. These are vendor measurements, and counts depend on the model, prompt and settings.
Do fan-out queries have search volume?
Mostly no. Seer found 95% of Gemini 3 fan-outs had zero global search volume, and a larger tracked set showed 99%. That does not mean zero demand, only that keyword tools don't see it.
Does ChatGPT use query fan-out?
ChatGPT appears to generate sub-queries when web search is on; DataForSEO reported that 47.5% of 100,000 prompts triggered it. OpenAI does not publish how it works, so treat details as inference.
Do I need new content or just better headings?
Check coverage first. If a sub-question is already answered but buried, fix structure. If nothing on your site answers it, add a section or a page.
Will covering fan-outs guarantee citations?
No. The studies show association, not causation, and AI answers vary between runs. It improves your chance of being retrievable.
Is this different from topical authority?
They overlap. Topical authority is about depth across a subject; fan-out coverage is a way of checking that depth against the specific questions an AI system is likely to ask.
How often should I redo the coverage map?
Quarterly for key topics, or after a major model change. Keep dated notes so you can compare.
Let's map your topics
If you'd like help auditing your most important topics for fan-out coverage, see my work and use the contact form at younusfardeen.in. I bring 4+ years of marketing experience in SEO and AEO and I will keep the plan practical, evidence-based and honest about uncertainty.