Query fan-out explained: what it means for your content

Ask Google’s AI Mode “is a heat pump worth it for an old house in a cold climate?” and it does not run that one search. It splits the question into parts (cold-weather performance, running costs, insulation, radiators, grants) and searches for each of them at once. The answer you read is assembled from the results of all those searches, and the sources it cites can come from any of them.

That process is called query fan-out. This explainer defines it, walks through the heat pump example in full, and shows what it changes about how you plan content.

What is query fan-out?

Query fan-out is the technique AI search systems use to break a question into related sub-queries, run them in parallel, and combine the results into one answer. Google says it is “a set of concurrent, related queries generated by the model to request more information and fetch additional relevant search results” (Google Search Central), and that AI Overviews and AI Mode may use it.

When Google rolled AI Mode out across the US in May 2025, it described the technique as “breaking down your question into subtopics and issuing a multitude of queries simultaneously on your behalf”. It added that Deep Search uses the same technique taken further, issuing hundreds of searches for a single request (Google, May 2025). The glossary entry for query fan-out has the short version.

A worked example: one question, eight searches

Here is how the heat pump question might fan out. Google does not publish the sub-queries it generates, so this table is illustrative: it shows the kind of expansion that happens, built from the angles AI answers to this question typically cover.

Original question: is a heat pump worth it for an old house in a cold climate?

#	Sub-query	What it is trying to establish	Page that answers it best
1	air source heat pump efficiency below freezing	Does it work in the cold?	Technical explainer with performance data
2	heat pump vs gas boiler running costs	Will it save money?	Cost comparison with worked numbers
3	heat pump insulation requirements older homes	Is the house suitable?	Suitability guide or checklist
4	do heat pumps work with existing radiators	What else needs changing?	FAQ section or installer guide
5	heat pump installation cost	What is the upfront cost?	Pricing page or cost guide
6	heat pump grants and rebates	Is help available?	Official government or utility page
7	heat pump noise neighbours	What are the downsides?	Review or FAQ
8	best heat pumps for cold climates	Which products qualify?	Product comparison or category page

Now consider three websites:

• Site A has one page targeting “is a heat pump worth it”. It competes for a single sub-query, and only if that phrasing is among the searches run.
• Site B has a thorough guide that answers sub-queries 1, 3 and 4 in clearly headed sections. It has three chances to be retrieved and cited.
• Site C has the cost comparison with real numbers (sub-query 2). It may be cited even though it never mentions “is it worth it”, because it is the best answer to one part of the question.

The engine then selects passages from the combined results and writes the answer. A source that wins one sub-query is as citable as one that wins the head term.

Which AI search systems use query fan-out?

• Google AI Mode and AI Overviews. Google documents the technique and says both features may use it.
• ChatGPT. In an analysis of 1.4 million prompts, Ahrefs had access to the sub-queries ChatGPT generated during search. Cited pages’ titles matched those fan-out queries more closely (a similarity score of 0.656) than they matched the user’s original prompt (0.602) (Ahrefs, April 2026). Research shows this as a correlation, measured with open-source embedding models.
• Perplexity. Perplexity describes retrieving and reranking individual passages through several stages (Perplexity, September 2025) but does not use the term fan-out in that write-up. Our view: the effect on what you should publish is the same, because each passage competes on how well it answers one specific question.

What does query fan-out mean for content coverage?

Query fan-out means a page is retrieved for the narrow questions it answers well, not only for its main keyword. Coverage of a topic’s distinct sub-questions, each answered clearly under its own heading, gives you more ways into an AI answer than ranking for one head term does.

Four practical consequences:

1. Plan around sub-questions, not just keywords. A keyword list tells you what people type into a search box. A sub-question list tells you what an AI system will search for on their behalf.
2. Answer each sub-question in its own section. Retrieval works at the level of passages, so a section titled “Do heat pumps work with existing radiators?” that answers in its first two sentences can be lifted on its own.
3. Name the sub-topic plainly in titles and headings. The Ahrefs data suggests the match between a page’s title and the generated sub-queries is one of the clearer signals separating cited from uncited pages.
4. Distinct questions, not reworded ones. Google says you do not need to chase long-tail keyword variations, because its systems understand synonyms. Fan-out coverage is about different questions (cost, suitability, noise), not ten phrasings of the same one.

What should you avoid?

Do not create a page for every sub-query. Google says creating separate content for every variation of how people might search, primarily to manipulate rankings or AI responses, violates its scaled content abuse policy. Thin pages that each answer one fragment also make weaker sources than a single page that answers the related questions together.

Our view: a sub-question earns its own page when it has its own searchers and needs a substantial answer, such as a full cost comparison. Otherwise it belongs as a section of the most relevant existing page.

How do you map fan-out for your own topics?

1. Pick your head prompts. Ten questions that matter commercially, phrased the way customers ask them.
2. List the sub-questions. Sources: People Also Ask and related searches, your sales and support inbox, and the angles covered when you run each prompt in AI Mode and ChatGPT. Note which sources those answers cite for each angle.
3. Build a coverage table. For each sub-question, record the page and section that answers it, and mark it as covered well, covered thinly, or missing.
4. Fill gaps in order of value. Add sections to strong existing pages first. Create a new page only for sub-questions that pass the test above.
5. Fix titles and headings. Make sure each page’s title and each section’s heading name the sub-topic in plain words. The meta tag checker shows how your titles render.
6. Re-run the prompts in a month and compare which sources are cited for each angle.

What to do next

Take your single most valuable question and do the mapping exercise for it alone: eight to ten sub-questions, a coverage table, and the two or three gaps that matter most. That one exercise usually shows whether your content is organized around keywords or around the questions buyers actually ask. For how this plays out in Google’s AI features specifically, see AI Overviews optimization; for the broader discipline, read the answer engine optimization guide.

Frequently asked questions

What is query fan-out in simple terms?

Query fan-out is when an AI search system breaks one question into several narrower searches, runs them at the same time, and builds its answer from the combined results. Google uses the term for AI Mode and AI Overviews; other answer engines such as ChatGPT also generate sub-queries before answering.

How many sub-queries does a fan-out generate?

Google has not published a number for standard AI Mode or AI Overviews. It has said Deep Search, a research mode in AI Mode, can issue hundreds of searches. The count likely depends on how complex the question is.

Should I create a page for every fan-out query?

No. Google warns that creating separate content for every variation of a search, mainly to manipulate rankings or AI answers, violates its scaled content abuse policy. Cover sub-questions inside strong existing pages, and create a new page only when a sub-question has its own audience and a substantial answer.

Can I see the fan-out queries for my topic?

Not directly from Google. You can approximate them from People Also Ask, related searches, your sales and support questions, and the angles covered in AI answers for your prompts. Some prompt-tracking tools report the sub-queries they observe for the prompts they run.