When someone types a single detailed question into Google's AI Mode, Google does not just run that one search. It breaks the question into smaller pieces, searches for each, and assembles the results into one answer. Google calls this query fan-out, and it has a direct consequence for content: the pages that get cited are often the ones answering the sub-questions, not the headline query.
That changes how you plan content. Instead of asking "what keyword does this page target?", you also need to ask "which sub-questions would an AI system generate from our customers' prompts, and do we answer them clearly?"
This guide explains what Google has actually documented about query fan-out, how to map the sub-questions for your own topics, and a practical way to build and audit coverage.
Key Takeaways
- Query fan-out is documented by Google: AI Mode breaks a question into subtopics and issues multiple related searches, and AI Overviews may use the same technique.
- Google has not published how many sub-queries a prompt produces or how they are chosen, so treat all third-party counts as estimates.
- Map sub-questions by intent: definitions, comparisons, costs, constraints, risks, steps and local or time-specific details.
- Cover sub-questions with clearly headed, self-contained sections and supporting pages linked from a hub.
- Measure progress by checking which of your URLs are cited for prompts and sub-prompts over time.
What Google has said about query fan-out
Google introduced the term publicly when it expanded AI Mode in May 2025:
“AI Mode uses our query fan-out technique, breaking down your question into subtopics”
— Google, AI Mode announcement (20 May 2025), Google: AI Mode update announced at I/O, 20 May 2025
The same announcement described Deep Search, which applies the technique at a larger scale and "can issue hundreds of searches". Google's Search Central documentation on AI features (updated December 2025) says both AI Overviews and AI Mode may use a "query fan-out" technique, issuing multiple related searches across subtopics and data sources to develop a response. You can read our overview of Google AI Mode for the wider context.
What Google has not published is how many sub-queries are run, how they are worded, or how results are weighted. Semrush's August 2026 explainer estimates 8 to 20+ background searches for a complex query but notes Google hasn't published exact counts. Any tool that claims to show "the" fan-out queries is showing a model's prediction, not Google's internal list.
A worked fan-out example
Take the prompt: "Best accounting software for a five-person UK design agency that needs to handle VAT and project costing, under £50 a month." A plausible set of sub-questions looks like this:
| Sub-question type | Example sub-query | Page or section that could answer it |
|---|---|---|
| Category / options | accounting software for small agencies UK | Category or comparison page |
| Compliance constraint | Making Tax Digital compatible accounting software | Compliance section with clear yes/no |
| Feature constraint | accounting software with project costing | Feature page with specifics |
| Budget constraint | accounting software price per month for 5 users | Pricing page with per-user detail |
| Comparison | [Product A] vs [Product B] for agencies | Head-to-head comparison page |
| Proof / reviews | [Product] reviews from agencies | Case studies, third-party reviews |
| Implementation | how to migrate from spreadsheets to [product] | Help-centre guide |
A vendor whose site only has a generic homepage and a feature list is unlikely to match many of these. A vendor with a clear pricing page, an MTD compliance statement and agency-specific comparisons can be retrieved for several sub-queries at once.
Step-by-step: map and cover sub-questions
- Collect real prompts. Use sales calls, support tickets, site search and chat logs to write 20–50 prompts in customers' own words. Our prompt research guide shows how.
- Decompose each prompt. List the conditions it contains (audience, budget, location, feature, timeframe) and the questions each condition implies.
- Observe the platforms. Run the prompts in AI Mode, ChatGPT and Perplexity. Note follow-up suggestions, the sources cited and any sub-topics the answer covers. Combine with People Also Ask and related searches.
- Group sub-questions by intent using the table above, then de-duplicate.
- Map each group to a URL. Decide whether it deserves its own page or a clearly headed section on an existing page. A pillar and cluster structure works well.
- Write self-contained answers. Open each section with a direct answer (see answer-first writing) and include the specific facts the sub-query needs.
- Link the cluster. Connect hub and supporting pages with descriptive anchor text so crawlers and readers can follow the path.
- Audit coverage quarterly. Re-run prompts, log which URLs are cited, and fill gaps.
Coverage matrix: how to judge a gap
| Status | What you see | Action |
|---|---|---|
| Covered and cited | Your URL is cited for the prompt or its sub-topic | Keep fresh; monitor |
| Covered, not cited | You have the content, but competitors or third parties are cited | Sharpen the answer, add specifics and evidence, improve internal links |
| Partially covered | Mentioned in passing inside a long page | Give it a heading and a direct answer, or a dedicated page |
| Not covered | No page addresses the sub-question | Create content if it matters to buyers |
| Not yours to win | Answer relies on independent reviews or news | Earn third-party coverage instead |
What to put in sub-question content
- Explicit conditions. "Works for teams of up to 10", "available in the UK", "from £X per user per month".
- Comparisons with named alternatives, fairly written.
- Numbers with context: units, dates, sources.
- Clear yes/no statements for compliance and compatibility questions.
- Proof: examples, case data, reviews and author expertise.
Semrush's own experiment, described in the same article, reported a page's tracked citations rising after it expanded sub-query coverage; the authors call this directional evidence rather than a settled result, which is the right way to read any single test.
Fan-out beyond Google
Google is not the only system that turns one prompt into several searches. OpenAI's ChatGPT search help page says ChatGPT typically rewrites a question into one or more targeted queries for its search partners and may send follow-up queries after reviewing the first results. Semrush's 2026 AI search guide likewise recommends covering the related angles AI tools explore. The practical lesson is the same across platforms: long, multi-condition prompts get split up, and each condition is a chance for a specific page to be retrieved. We explain how those prompts differ from keywords in writing for long, conversational prompts.
Common mistakes
- Treating fan-out tool output as fact. Predicted sub-queries are useful ideas, not Google's real list.
- Creating a thin page for every micro-question. Many sub-questions are better answered as sections of a stronger page.
- Ignoring commercial sub-questions. Price, contract terms, integrations and refunds are frequent conditions in buying prompts.
- Burying answers in long paragraphs. Sub-query matching favours clear, headed passages.
- Forgetting off-site sources. Some sub-questions (reviews, "is X reliable?") are answered from third-party sites.
- Never re-checking. Prompt behaviour changes; audit regularly with a repeatable AI citation audit.
Related Guides
- Writing for Long, Conversational Prompts
- Statistics Pages and Proprietary Data: Why AI Cites Numbers
- Author Signals in AI Search: Do Bylines and Credentials Matter?
Frequently Asked Questions
What is query fan-out?
It is a technique Google uses in AI Mode, and may use in AI Overviews, that breaks a question into subtopics and runs multiple related searches before combining the results into one answer.
How many searches does query fan-out run?
Google has not published a number for standard AI Mode. It says Deep Search can issue hundreds of searches. Third-party estimates, such as Semrush's 8 to 20+, are estimates.
Can I see the exact fan-out queries for my topic?
Not from Google. Tools that list fan-out queries generate predictions with a language model. Use them as ideas alongside real customer questions.
Does query fan-out mean long-form content wins?
Not automatically. What helps is clear coverage of the sub-questions, whether in well-headed sections of one page or a linked cluster of focused pages.
Do other AI tools use fan-out too?
OpenAI says ChatGPT search rewrites questions into one or more targeted queries and may send follow-up queries, which is a similar idea, though it does not use Google's term.
Answer the questions behind the question
Query fan-out rewards sites that answer the specific conditions buyers care about, clearly and in the right place. Start with ten real customer prompts, decompose them, and fill the most valuable gaps first. If you would like help building a sub-question map and content plan, our content writing team can help. Request a quote.
