Query fan-out

How does query fan-out work?#

The engine reads the question, writes several narrower searches that together cover it — sub-topics, comparisons, specifics like price or location — runs them in parallel, and writes one answer from the combined results.

Google introduced the name when it launched AI Mode, saying it uses a “query fan-out technique” that breaks a question into subtopics and issues a multitude of queries simultaneously. OpenAI describes the same step for ChatGPT search: it “typically rewrites your query into one or more targeted queries.” Both then rerank the pooled results and cite the passages they use.

1. 1

   The user asks one question

   “What’s the best CRM for a 10-person startup?”

2. 2

   The engine fans it out

   It writes narrower searches: best CRM for small teams this year, CRM pricing per user, easiest CRM to set up, specific product comparisons — and, increasingly, site: searches of vendors’ own domains.

   Your lever: Have a clearly titled page for each sub-question a buyer checks: pricing, setup, integrations, comparisons.

3. 3

   Results are pooled and reranked

   Candidates from every sub-query compete in one pool. A page that answers one sub-query well can beat a page that ranks for the original question.

   Your lever: Answer the sub-question in the section’s first sentence.

4. 4

   The answer cites the passages it used

   Citations point at the pages behind each claim, which is why the cited list rarely matches the classic top ten.

How many searches does an AI engine run per question?#

It varies by engine and by question: ChatGPT jumped from about two sub-queries per prompt to about 7.6 in August 2026, and Google says Deep Search in AI Mode can issue hundreds of searches for one question.

The August 2026 shift matters most for brands: site: fan-outs go straight to domains the engine already treats as authoritative — the vendor itself, regulators, standards bodies — and “official” became one of the most common words in ChatGPT’s sub-queries. The fastest way into an answer about your product is now your own clearly titled page.

Query fan-out vs long-tail keywords: what's the difference?#

Long-tail keywords are narrow queries people type; fan-out queries are narrow queries the engine writes on the user’s behalf — so you can’t see them in a keyword tool, but they reward the same thing: specific pages for specific questions.

Classic long-tail keyword research starts from search volume. Fan-out has no published volume: sub-queries are generated fresh for each prompt and rarely logged anywhere you can access. The practical workaround is to reason from the buyer’s decision — what would they need to check before choosing? — and to watch which pages get cited in prompt tracking.

How do you optimize for query fan-out?#

Map the sub-questions a buyer’s prompt would trigger, make sure each one has a page or a clearly headed section that answers it in the first sentence, and title those pages the way the engine would search.

1. List the decision questions behind each buyer prompt: price, setup time, integrations, who it’s for, alternatives, proof.
2. Build official pages for facts buyers verify — pricing, specs, policies, comparisons — so site: searches land on your domain, not a reseller’s.
3. Title for the sub-query: natural-language titles, H2s and slugs that read like the search (“CRM pricing per user”), not clever headlines.
4. Write answer-first so the matching passage can be lifted without the paragraphs around it.
5. Link the cluster together with internal links, so an engine that lands on one page can reach the rest.

Related terms#

• AI search & GEOAI ModeA conversational search experience inside Google Search that answers complex questions with a generated response built from many parallel sub-searches and supports follow-up questions, rather than summarizing above the classic results as AI Overviews do.Read the entry
• Content & relevanceLong-tail keywordsSpecific search phrases that each draw few searches but together make up the vast majority of distinct queries, and because they signal precise intent they are the closest classic-SEO match to the conversational prompts people type into AI.Read the entry
• How LLMs answerRerankingA second retrieval stage in which a more precise model re-scores the top results of a first, faster search against the query and reorders them — the step that decides which few passages an AI answer engine actually reads and cites.Read the entry
• RAGHow LLMs answerRetrieval-augmented generationA technique in which an AI system first retrieves relevant documents from an index and then gives them to a large language model to write its answer, so the response can cite current sources instead of relying only on what the model memorized in training.Read the entry
• Content & relevanceTopic clusterA group of interlinked pages on one subject — a broad pillar page linked to and from narrower pages on each subtopic — built to show search engines and AI retrieval systems complete coverage of the topic rather than a single article.Read the entry
• GEOAI search & GEOGenerative engine optimizationThe practice of making content easy for AI answer engines such as ChatGPT, Perplexity and Google AI Overviews to retrieve, quote and cite — where SEO competes for a ranked link, GEO competes to be a named source inside the generated answer.Read the entry