What does Google say about query fan-out?

Google's documentation for site owners says both AI Overviews and AI Mode may use a query fan-out technique. It explains this as "issuing multiple related searches across subtopics and data sources" (Google Search Central: AI features and your website, last updated 2025-12-10, read 1 November 2026).

Google first described it when it launched AI Mode. Elizabeth Reid, Google's Head of Search, wrote that AI Mode "uses our query fan-out technique, breaking down your question into subtopics" and "issuing a multitude of queries simultaneously on your behalf". Deep Search "uses the same query fan-out technique but taken to the next level" (Google blog, 20 May 2025, read 1 November 2026).

Do other AI assistants fan out queries too?

"Query fan-out" is Google's name. Others describe the same idea in their own words. OpenAI's docs say that with agentic search a model "can perform web searches as part of its chain of thought, analyze results, and decide whether to keep searching" (OpenAI: web search, read 1 November 2026). Google's Gemini API docs say the model "automatically generates one or multiple search queries and executes them" when a search would help (Gemini API: grounding with Google Search, last updated 2026-10-28).

Bing describes a related loop as part of grounding: a system "may need to ask follow-up questions, refine retrieval based on intermediate results" (Bing blog, 6 May 2026).

How does query fan-out work?

  1. The user asks one question, often a long one.
  2. The system splits it into sub-questions.
  3. It runs a search for each sub-question, at the same time.
  4. It reads the results and picks the passages that answer each part.
  5. It writes one answer and links the pages that support it.

What does query fan-out look like? (hypothetical example)

A buyer asks AI Mode: "Which CRM is best for a 10-person B2B SaaS sales team that uses HubSpot for marketing and needs Slack?" A fan-out could produce searches such as "best CRM for small B2B sales teams", "CRM with HubSpot marketing integration", "CRM Slack integration" and "CRM pricing for 10 users". These searches are our illustration, not queries Google has published.

Why does query fan-out matter for B2B SaaS?

Your page does not need to rank for the long question. It needs to be a good result for one of the sub-searches. A page that clearly answers "CRM with Slack integration" can be used even if it never ranks for the full question.

Google says there is no special markup for this. To be a supporting link in AI Overviews or AI Mode, "a page must be indexed and eligible to be shown in Google Search with a snippet".

Many sub-searches have the shape "best X for Y", and lists answer that shape well, because one page covers many options. That fits what Prefer measured in July 2026: 40% of the sources cited for buyer questions were independent lists. A brand that is in the lists for each sub-need has more chances to be in the combined answer. Google decides what it shows.

How do you write for query fan-out?

  • List the sub-questions a buyer has: price, integrations, team size, setup time, alternatives.
  • Give each one its own heading and a direct answer in the first sentence.
  • Keep facts current and dated, such as prices and plan limits.
  • Get into the third-party lists that rank for the "best X for Y" versions of your category.
  • AI Mode is a product. Query fan-out is one technique it uses.
  • Grounding is the goal: an answer that rests on sources. Fan-out is how the system collects those sources for a complex question.
  • Retrieval-augmented generation is the general pattern of retrieving text first and generating second. Fan-out runs that retrieval many times for one question.

For the full picture of how brands get named in AI answers, see the LLM SEO guide.