Query Fan-Out

What does Google actually say about query fan-out?

Google introduced the phrase itself, which is rarer in this field than it sounds. Announcing AI Mode on 20 May 2025, Google described the query fan-out technique as “breaking down your question into subtopics and issuing a multitude of queries simultaneously on your behalf”. Google Search Central now repeats it in AI features and your website, describing fan-out as “issuing multiple related searches across subtopics and data sources” and attributing it to both AI Overviews and AI Mode. Deep Search, the longer-running mode, “can issue hundreds of searches” for a single question.

What Google has never published is a per-query count. There is no documented number of sub-queries for an ordinary AI Overview, so treat any specific figure you read as invented. That absence is the honest state of the record, and it is worth more than a number somebody made up.

Why does query fan-out change what a page has to cover?

Query fan-out moves the unit of competition from the question to the sub-question. A page is retrieved because it matched one of the searches the engine issued on the reader’s behalf, and those searches are generated rather than typed: they carry modifiers, comparisons and qualifiers nobody entered. Moz’s study of 40,000 queries found that 88% of Google AI Mode citations do not match the organic top 10 for the same query, which is the measurable shape of exactly this. Ranking first for the head term is neither required nor sufficient.

The practical consequence is coverage rather than length. Every adjacent question a page answers is a sub-query it can be retrieved for, and every one it leaves out is a sub-query answered by somebody else’s page in the same response. That is an argument for one clear section per sub-intent, phrased the way the question gets asked, and against padding: a section that answers nothing is retrievable for nothing.

Can you see the sub-queries a fan-out fired?

Mostly no, and any tool showing you a tidy list should say which half of this it is doing. Some platforms return their own search queries through the API: Perplexity exposes a search_queries field alongside the answer, and Google’s Gemini grounding API returns the queries the model ran in a google_search_call block. Those are observed fact. Every other assistant returns the answer and nothing else, so the sub-queries behind it can only be inferred from the response text and the sources it cited.

RankX AI stores that distinction on every tracked answer rather than hiding it: each record carries the queries and a source of native, inferred or none. Native means the platform handed them over. Inferred means a second model read the answer and proposed what was probably searched. They are not the same evidence and they are not labelled as though they were.

One more limit worth stating plainly: fan-out is generated per run, so the same prompt does not necessarily produce the same sub-queries twice. Anything built on a single observation of a single run is a screenshot, not a measurement.

Related terms

• GroundingGrounding is the step that ties a model’s answer to sources it retrieved, so the reply can cite them rather than assert them.
• ChunkingChunking is the step that splits a page into passages so a retrieval system can select one without the rest.
• AI OverviewAn AI Overview is the written summary Google places above its ordinary search results, assembled from web pages and shown with links to a few of them.
• Google AI ModeGoogle AI Mode is a conversational search surface where a written answer replaces the ranked results page rather than sitting above it.