What is query fan-out in Google AI Mode?

Query fan-out is the technique Google AI Mode uses to treat a search not as a single question, but to split it into multiple smaller sub-queries that run at the same time. Instead of one list of results, the system retrieves separate sources for each sub-question and builds a single composite answer from them. In this article you will read what query fan-out precisely is, how it works, how Google chooses sources in the process, and what that means for your findability in AI.

What is query fan-out?

Query fan-out is the process by which Google AI Mode unravels your original question into a series of related sub-questions and searches all of them at once. Google introduced AI Mode at I/O 2025 and describes the technique itself as breaking a question down into subtopics, with the system issuing “a multitude of queries at once” on behalf of the user.

The difference with classic search is fundamental. In the past you typed one search term and got back one set of blue links. With query fan-out, that same question is internally translated into multiple queries, each producing its own results. The model reads the relevant pieces from them and composes an answer with them. One question from you becomes, behind the scenes, a handful of queries for Google.

An example makes it concrete. Suppose someone asks: “which CRM suits a small B2B team in the Benelux best”. AI Mode then does not only search that exact phrase. It can fire off sub-questions in parallel like “best CRM for small businesses”, “CRM with Dutch-language support”, “CRM prices for small teams” and “CRM integrations for B2B”. For each of those sub-questions the system gathers sources, and the final answer is a synthesis of what it found across all those sub-questions.

How does query fan-out work technically?

AI Mode runs on a customized version of Gemini, Google’s most advanced model, which first analyzes the question before it starts searching. The model estimates how complex your question is, what your intent is and what type of answer is needed. Based on that, it determines into how many sub-questions it splits the original question.

Not every question activates the same degree of fan-out. A simple factual question, such as the capital of a country, hardly calls for any splitting. A complex, comparative or multi-layered question triggers an extensive fan-out precisely because there are several facets the model wants to cover separately. The more composite your question, the more sub-queries emerge from it.

The sub-questions are executed in parallel, not one after another. That allows the system to explore far more of the web in a short time than a human with one classic search would. Google applies the same technique in a reinforced form in its more in-depth research feature, which according to Google can fire off up to hundreds of queries and then reason across all those separate pieces of information to build an extensive, well-founded answer.

Important: the exact sub-questions Google generates are not something you see directly as a user or brand. You cannot “target” them like a keyword. What you can steer is how well your content matches the kind of sub-questions that logically follow from a topic.

How does AI Mode choose sources per sub-question?

For each sub-query, AI Mode selects passages, not whole pages. The model pulls relevant pieces, often called “chunks”, from different web pages and feeds them into the generated answer. That means one answer is almost always composed of multiple sources, because each sub-question can have its own best source.

The principles that determine which passage gets chosen lie close to what good findability has always been about. Relevance for the specific sub-question weighs heavily: a passage that answers exactly that one question beats a page that only vaguely touches the topic. Clarity and structure count, because the model has to be able to read your text and lift a delimited piece out of it. And trust plays a role, because the model prefers to cite a source it assesses as reliable. How AI decides more broadly which information is true and reliable, you read in grounding: how AI determines what is true.

For you as a brand, this is where the heart of the story lies. You do not need to “win” the entire search. It is enough that your passage gives the clearest, best answer to one sub-question within the larger topic. That is a fundamentally different way of thinking than chasing one top position for one keyword.

What does query fan-out mean for your findability?

Query fan-out makes the structure of your content more important than ever, because the model is looking for one sharply delimited answer per sub-question. Whoever builds their pages around clear sub-questions, with a quotable answer per question, makes it easy for AI Mode to lift exactly that fragment and attribute it to you.

A few practical consequences.

Think in sub-questions, not in keywords. Map out which related questions a buyer asks around your topic and answer each of them explicitly. Those are the sub-questions fan-out will likely arrive at. How to build your content so a model can extract pieces from it, we cover in content architecture for AI extraction.

Give the answer early and explicitly. Put the essence at the top of a section in one clear sentence, and only then work out the nuance. A model that quickly finds the answer cites you sooner.

Cover the breadth of a topic. Because fan-out explores several facets at once, it helps if your site treats the topic thoroughly rather than superficially. A coherent cluster of pages covers more sub-questions than one standalone text.

Build demonstrable authority. Mentions elsewhere, a recognizable brand and credible references increase the chance that your passage is chosen as a source. That signal keeps carrying weight, even in a world of sub-queries.

Query fan-out is built into AI Mode, but leans closely against how Google AI Overviews build their summaries. It belongs in the broader practice of generative engine optimization: making sure AI systems include your brand in their answer, wherever that answer appears.

The short summary

Query fan-out is the technique Google AI Mode uses to split your question into multiple sub-queries, executed in parallel by a customized version of Gemini. Per sub-question the system chooses passages from different sources and composes one answer from them. For your findability the game therefore shifts: you no longer win one search, you deliver the best answer to one sub-question. Clear structure, explicit answers and real authority are the way to end up in that composite answer.

At Customer Impact we look further than visibility on its own. Being mentioned in an AI answer is not a goal, it is a means to obtain leads and revenue. That is where our findability in AI search engines and an honest, no-nonsense approach come together: we steer on what pays off, not on vanity figures, and we also name where the limits lie.

Schedule your free intake and you will hear where your opportunities lie to become visible in Google AI Mode and beyond.