Query fan-out is the technique AI search systems use to split one question into several smaller searches, run them at the same time, and combine the results into one answer. Google described it publicly when it introduced AI Mode in May 2025, and similar query rewriting happens in ChatGPT search, Perplexity and other assistants. It explains why a page can be cited for a question it never targeted.
How it works
Take the question: “Which CRM should a 20-person B2B sales team in India choose if we already use Tally and have a budget of 50,000 rupees a month?”
No single page answers all of that. A system using query fan-out might generate searches along these lines:
- CRM for small B2B sales teams
- CRM Tally integration
- CRM pricing per user India
- CRM comparison for 20 users
- CRM data residency India
It retrieves pages for each, selects the passages that answer each part, and writes a combined recommendation. The sub-queries are generated by the model, so they can include needs the user did not state, such as data residency in this example.
The related term query decomposition comes from research on multi-step question answering, where a complex question is broken into simpler questions answered in order. Fan-out runs the pieces in parallel; decomposition can run them in sequence, using the answer to one to form the next. Production systems use both.
What Google has said
At Google I/O in May 2025, Google explained that AI Mode breaks a question into subtopics and issues many queries at once, and that its Deep Search feature can run hundreds of searches for one request. AI Overviews use related techniques. Google has not published the exact rules for generating sub-queries, and they change with the question.
What it means for your website
You compete on sub-questions
Most sub-queries are narrower than the original question. A page that answers “CRM Tally integration” clearly has a chance to be cited in the combined answer, even if it never mentions team size or budget.
Coverage of a decision matters more than one big page
Buyers’ questions bundle several needs: price, fit, compatibility, limits, location. A site that answers each need on a clearly structured page, or in clearly separated sections, gives the system more matching passages than a single general page.
Unstated conditions become searches
Because the model adds needs the user did not type, details such as who a product is for, where it is available, what it integrates with and what drives the price can match sub-queries you would never find in keyword research.
How to find the sub-questions for your topics
There is no tool that shows the exact sub-queries an assistant generated. We build the list from several sources and check it against real answers:
- “People also ask” and related searches in Google for your main topics
- Questions from sales calls, support tickets and enquiry forms
- Asking assistants the full buyer question and noting which aspects their answers cover and which sources they cite for each
- Search Console queries that show impressions for long, specific questions
Each aspect that keeps appearing becomes a section or a page, with the answer at its start.
Frequently asked questions
Is query fan-out the same as keyword research?
No. Keyword research looks at what people type. Fan-out is about what the system searches for on their behalf, which is often more specific and includes needs the person never stated.
Does fan-out mean long pages rank better in AI answers?
Not by itself. Long pages help only when each section answers a distinct sub-question clearly. A long page that circles one idea does not match more sub-queries.
Can I see the sub-queries in Search Console?
Google has not confirmed that AI Mode sub-queries appear as separate queries in Search Console. Unusual long-tail queries there can be a useful hint, but they will be an incomplete list at best.