What is query fan-out?
Google made the term widely known with AI Mode. At Google I/O in May 2025, Google explained that AI Mode breaks a question down into subtopics and issues a multitude of queries simultaneously on the user’s behalf (Google blog). This is meant to let the system dig deeper into the web than a single search would.
In its guide to generative AI search, Google defines query fan-out as a set of concurrent, related queries generated by the model to request more information and fetch additional relevant search results. Google’s own example is the query ‘how to fix a lawn that’s full of weeds’, with fan-out queries such as ‘best herbicides for lawns’, ‘remove weeds without chemicals’ and ‘how to prevent weeds in lawn’.
How query fan-out works
A simplified example: someone asks “Which heat pump suits a 1970s detached house, and what will it cost?”. A system using query fan-out might derive sub-searches such as:
- heat pumps in older buildings: requirements and flow temperature
- air source versus ground source heat pumps
- costs of purchase and installation
- current grants and funding schemes
The results of these searches are combined and condensed into one answer. Google does not publish which sub-searches it actually runs. For Deep Search, Google said at I/O 2025 that it can issue hundreds of searches.
Where Google uses query fan-out
According to Search Central, both AI Overviews and AI Mode may use query fan-out, issuing multiple related searches across subtopics and data sources. Google says this lets it identify more supporting pages while the answer is being generated and display a wider and more diverse set of links than a classic web search.
Google described the technique again when launching AI Mode in the UK in July 2025 (Google blog UK). This article describes Google’s documented implementation.
What query fan-out means for content
- Visibility through sub-questions: a page can appear as a source because it answers a sub-question well, even if it does not rank for the original question.
- Cover topics fully: content that addresses requirements, costs, options and typical objections offers more points of contact for sub-searches.
- Clear sections: unambiguous subheadings and precise statements make it easier to match a section to a sub-question.
- Internal linking: related pages should link to each other so that connections are clear.
At the same time, Google stresses that content does not need to be broken into small chunks or rewritten specifically for AI in order to appear in generative search (guide). The point is completeness and clarity, not a new text format. Google also warns against creating separate pages for every possible sub-question: doing so primarily to manipulate rankings or AI responses violates its scaled content abuse spam policy.
Limits of analysis and measurement
Website owners cannot see the sub-queries themselves. According to the help page, the Generative AI performance report in Search Console shows impressions by page, country, device and date, but no queries. Tools that predict fan-out queries therefore work with estimates, usually generated by a language model. They are useful for ideas but do not reflect what Google actually searches for.
Query fan-out in practice
A workable approach is a question map: for each core topic, collect the sub-questions customers raise in sales conversations, emails or search. Long queries in Search Console provide further clues. Then check which sub-questions your website already answers clearly and where the gaps are. This analysis is part of our GEO audit, and implementation is part of AI SEO.
Would you like to structure your content so that it also answers the sub-questions behind a search and need support? Then get in touch or read more about our AI SEO service! Would you like to find out more or have your team trained on this topic? Then take a look at our GEO seminar.
