Query Fan-Out

How it works

A classic search engine takes the words you typed and returns one ranked list. A system that uses query fan-out first works out what the question actually involves, breaks it into narrower questions, runs searches for each of them, and then writes one answer from everything it found.

Google's own example, from its March 5, 2025 introduction of AI Mode, is a question about the difference in sleep tracking features between a smart ring, a smartwatch and a tracking mat. Google said the model "uses a multistep approach to make a plan, conduct searches to find information and adjust the plan based on what it finds" (Google). One question becomes several retrievals, and the user sees a single response with links.

This is a form of retrieval-augmented generation: the model's answer is grounded in documents fetched at question time, and fan-out simply widens how many separate fetches go into one answer.

What Google has said

Google has described the technique in its own words several times, and the details matter more than the summaries that circulate.

• March 5, 2025. Announcing AI Mode as a Labs experiment, Google said it "uses a 'query fan-out' technique, issuing multiple related searches concurrently across subtopics and multiple data sources and then brings those results together." It named the Knowledge Graph and shopping data alongside web content as sources (Google).
• May 20, 2025. At I/O, Google began rolling out AI Mode in the US without a Labs sign-up and said that "under the hood" it breaks your question "into subtopics" and issues "a multitude of queries simultaneously on your behalf." It also announced Deep Search for AI Mode, which "uses the same query fan-out technique but taken to the next level" and "can issue hundreds of searches" to produce a cited report (Google).
• November 18, 2025. With Gemini 3 in Search, Google said the fan-out technique was "getting a major upgrade" and can "perform even more searches," finding content "it may have previously missed" (Google).

Google's documentation for site owners says both AI Overviews and AI Mode "may use" query fan-out, and that this lets Google "display a wider and more diverse set of helpful links associated with the response than with a classic web search." The same page says there are no additional requirements or special optimizations needed to appear in either feature (Google Search Central).

Other AI search systems do something similar

Google coined the label, but the pattern is not unique to it. OpenAI's help center says that when ChatGPT search uses outside search providers, it "typically rewrites your query into one or more targeted queries," and may then send "additional, more specific queries" after reviewing the first results (OpenAI). OpenAI does not call this fan-out, and it has not published how many queries a typical answer uses.

Because the system writes its own searches, the sources behind an answer can shift between runs of the same prompt. Our analysis of practitioner research into ChatGPT's retrieval pipelines covers how much cited sources move when nothing about the question changes.

Why pages get cited for questions they never targeted

This section is practitioner inference from what Google has stated, not something Google has spelled out.

If an AI answer is built from several sub-searches, a page only has to be a good result for one of them to end up among the links. Google's own sleep tracking example shows the shape: one comparison question becomes separate searches about each device and each feature. A page that answers one of those narrower questions well can be cited in an answer to a broader question it never mentions.

The reverse also holds. A page optimized for the exact headline query can be left out if other pages answer the sub-questions more directly. Google's statement that fan-out produces a "wider and more diverse set" of links than a classic search is consistent with this: more retrievals mean more chances for more pages, and less weight on any single ranking position.

What to do about it

1. Map the sub-questions, not just the head term. For each buying question, list what a careful buyer would need to know: cost, fit for their size, setup effort, risks, alternatives. Those are the likely sub-searches.
2. Answer each one in a self-contained section. Clear headings and a direct first sentence let a retrieval system pull one section without needing the rest of the page.
3. Publish honest comparisons. Google's own examples are comparison questions. Pages that compare options on named criteria map naturally onto how those questions get split.
4. Be specific. Numbers, constraints, supported integrations and clear eligibility rules answer narrow sub-queries that vague copy cannot. Our guide to content that gets cited covers these patterns.
5. Build depth across a topic. A connected set of pages on one subject, often called topical authority, gives more surface area for the many retrievals behind one answer.

Common misconceptions

"Fan-out queries are a new keyword list to target." Google does not publish the sub-queries it runs. Lists sold as fan-out queries are third-party reconstructions, useful for brainstorming and unreliable as data.

"It needs special markup or files." Google's guidance says no special optimizations are required for AI Overviews or AI Mode, and it names no fan-out-specific markup.

"Ranking first for the main query is enough." In a fan-out answer, the main query is only one of many retrievals, so pages that answer the surrounding questions also get chances to be cited. Google has not said how it weighs them.

Because answers are assembled from many searches, measuring visibility means tracking the full set of questions buyers ask across engines, repeated over time, rather than one keyword.