What is query fan-out?
fan-out queries, sub-queries, query expansion
Query fan-out is the process where an AI search engine breaks one user question into several narrower sub-queries, runs them in parallel, and combines the results into a single answer. Google AI Mode, ChatGPT search and Perplexity all use fan-out, so one prompt can draw on pages that rank for many different searches.
Query fan-out turns one question into many parallel searches.
A page can be cited for one sub-query even if it misses the original question.
Covering related sub-questions in depth raises your chances of being retrieved.
How query fan-out works
Query fan-out works by having the AI model plan its research before it searches. The model reads the question, identifies the facts it needs, and writes a set of sub-queries to find them. Each sub-query is sent to a search index, often at the same time, and the results are pooled.
The model then selects the most useful passages across all those results and writes one answer, the process described in retrieval-augmented generation. Some engines run a second round of searches if the first round leaves gaps.
Google has described fan-out as a core part of Google AI Mode, where a single question can trigger many searches across different subtopics. Other engines work in a similar way, although the number of sub-queries varies by engine and by how complex the question is.
Query fan-out example
A query fan-out example makes the idea concrete. Suppose a user asks an AI assistant: "What is the best CRM for a 10-person marketing agency?" The engine might split that question into sub-queries such as:
best CRM for small agencies
CRM pricing per user for small teams
CRM integrations with Slack and Google Workspace
HubSpot vs Pipedrive for agencies
CRM reviews from agency owners
Each sub-query returns its own set of pages. The final answer may cite a pricing page from one site, a comparison article from another and a forum thread for user opinions. A brand that only has a generic homepage competes for one of those slots at most. A brand with clear pricing, integration and comparison pages can appear in several of them at once, which builds AI share of voice.
Why query fan-out matters for your brand
Query fan-out matters because it changes what ranking means in AI search. In classic SEO, you target one keyword per page. With fan-out, the answer to one prompt is built from many searches, so visibility depends on how many of those sub-queries your content can satisfy.
Fan-out also explains why pages that rank modestly in traditional search can still be cited. If your page is the clearest answer to one narrow sub-query, the engine may select it even though it would not rank for the broad question.
This rewards topical authority. A site that covers a subject from many angles (pricing, setup, comparisons, use cases, limitations) gives the engine more chances to retrieve it across the fan-out, and more chances to shape how the final answer describes the category.
How to optimize for query fan-out
Optimizing for query fan-out starts with mapping the sub-questions behind your most important prompts.
List the prompts your buyers ask AI assistants, both branded and unbranded. See branded vs unbranded prompts.
Break each prompt down into the facts an engine would need: price, features, integrations, alternatives, reviews and fit for a specific audience.
Check coverage: does a page on your site answer each sub-question clearly? If not, create or expand one.
Write extractable answers: use headings that match the sub-question and open each section with a direct answer. See content chunking.
Earn third-party coverage for sub-queries where engines prefer independent sources, such as reviews and comparisons.
How KIME helps you cover fan-out queries
KIME tracks the prompts that matter in your category and records which sources each AI engine cites in its answers. When competitor pages and third-party articles keep winning for a topic, that points to sub-questions your content does not yet cover. KIME Actions turns those gaps into ranked content, technical and outreach tasks, so your team knows which pages to create or improve first.
Frequently asked questions
How many searches does an AI engine run for one question?
The number varies. A simple factual question may need a single search, while a complex comparison or research question can trigger many sub-queries, sometimes over several rounds. Engines do not publish fixed numbers, and the count depends on the engine, the mode the user selects and how much information the question requires.
Can I see the sub-queries an AI engine used?
Sometimes. Some engines show the searches they ran, for example in an expandable sources or research panel, but most do not show the full list. A practical approach is to infer likely sub-queries from the sources an engine cites: if it keeps citing pricing pages and review sites, those topics were probably among its searches.
Is query fan-out the same as query expansion?
They are related but not identical. Query expansion is an older search technique that adds synonyms or related terms to one query to improve recall. Query fan-out goes further: the AI model writes several distinct sub-queries, each aimed at a different part of the question, and combines the results from all of them into one answer.