Query Fan Out: What It Is and How Google's AI Search Uses It

Type a question into Google's AI Mode and you get one answer. Google says its AI systems may run several related searches across different subtopics and data sources, then use what they find to develop a response. Google calls this process query fan out.

What Is Query Fan Out?

Google's own Search Central documentation describes it directly: "Both AI Overviews and AI Mode may use a 'query fan-out' technique -- issuing multiple related searches across subtopics and data sources -- to develop a response." Google uses the term itself: when it launched AI Mode in May 2025, it called this "our query fan-out technique." The idea of "fanning out" one request into many is older and comes from computing, where one task is split into several that run at the same time.

The basic idea: a single, narrow search often can't gather everything needed to answer an open-ended question well. So instead of running your exact query once, the system can run several related searches, gather results from each, and then generate one answer that draws on all of them.

Why One Query Becomes Several

A short example makes this concrete. Someone types a broad question like "best running shoes for a first marathon." A traditional search would return results for that query. A system using query fan out can instead run several related searches behind the scenes: narrower versions ("running shoes for marathon beginners"), comparisons ("cushioned vs. minimalist shoes for long distance"), and follow-up angles someone researching this topic would likely want next ("how to break in new running shoes before a race"). The answer you see combines information gathered across all of those searches, not just the one you typed.

Different people describe the range of subquery types differently, and there's no single confirmed list from Google itself. In general terms, the related searches tend to include things like: rephrased versions of your question, narrower or broader versions of the same topic, comparisons between options, and questions someone would naturally ask next. The exact mechanics aren't fully public, so treat any specific, numbered breakdown you see elsewhere as one researcher's interpretation, not a confirmed Google specification.

How This Is Different From a Regular Search

In a traditional Google search, you enter one query and Google returns a set of results for it. With query fan out, a single page might be used in an AI answer if it provides relevant information for one of several related searches the system ran, not necessarily the exact phrase the person typed.

That changes what "matching the search" means. Ranking for one specific keyword phrase doesn't guarantee your page ends up in the mix, and a page that never ranked well for the exact original phrase can still get used if it answers one of the related searches well.

What This Means for Content, Realistically

There's no confirmed formula for getting a page pulled into a specific subquery, and no tool, including this one, can promise it. Query fan out is a retrieval technique, not a ranking factor you can optimize for directly. What's actually controllable is how thoroughly and clearly a page covers a topic. A few practical implications are worth considering, without treating any of them as ranking guarantees:

• Covering a topic's likely follow-up questions on the same page, or a well-linked set of pages, gives a system more of the related searches a chance to match something you've actually published.
• Clear subheadings that name a specific angle of the topic (a comparison, a narrower use case, a common follow-up question) make it easier for a system to identify which part of your page answers which subquery.
• Ranking well for a page's main keyword is still worth doing, but it isn't the same test as being useful for the related searches a fan out process might run. A page can be strong on one and weak on the other.
• This overlaps with why long-tail keyword coverage matters (see the Long-Tail Keywords guide) -- many of the "related searches" in a fan out process look like the same narrower, more specific phrases that guide covers from a traditional-search angle.

Don't create content for every hypothetical fan out query. Cover related questions when they are useful to your audience and fit the topic. Creating separate pages mainly to target every possible AI-generated search can lead to thin, repetitive content, and it isn't a shortcut to visibility in AI search.

Query Fan Out and Keyword Research

This doesn't replace keyword research, it adds a reason to take topic coverage seriously rather than optimizing for one exact phrase. The full process for finding and organizing keywords, including the related searches and follow-up questions worth covering, is in the Keyword Research guide. What query fan out adds to that process: when you're deciding whether a related phrase deserves its own coverage on a page, remember that an AI system may search for that exact related phrasing on its own, separately from whatever the person originally typed. Those internal searches aren't shown as ordinary Google Trends queries, so Trends isn't a way to see the fan out searches behind an AI answer (see Google Trends).

Query Fan Out in Google, ChatGPT and Other AI Tools

Google AI Mode and AI Overviews

When Google launched AI Mode in May 2025, it described query fan out as "breaking down your question into subtopics and issuing a multitude of queries simultaneously on your behalf." Its Deep Search feature takes the same idea further and can run hundreds of searches for one report. AI Overviews may use the technique too, according to Google's Search Central documentation (quoted above).

ChatGPT and other AI assistants

ChatGPT search does something similar. OpenAI says it "typically rewrites your query into one or more targeted queries" and sends them to its search providers, and it can send more refined searches after reading the first results. Other AI assistants with web search work in their own ways, and most don't publish the details. How AI systems then choose which sources to use is covered in How AI Engines Select Information and Sources in our GEO guide.

Query Fan Out Examples

Google doesn't publish the searches it runs for a given question, so most examples are educated guesses. One real example comes from OpenAI, which shows how ChatGPT search breaks down a research question: a question about CCR8 drug development became the search "CCR8 immunotherapy drug development 2025," then, after reading the first results, a narrower follow-up search, "CHS-114 conference 2025."

Here's how a fan out could look for everyday questions (illustrations, not confirmed Google searches):

Question someone asks	Related searches a system might run
best running shoes for a first marathon	running shoes for marathon beginners; cushioned vs. minimalist shoes for long distance; how to break in new running shoes before a race
is a heat pump worth it in a cold climate	heat pump efficiency below freezing; heat pump vs. gas furnace running cost; heat pump lifespan
best project management software for a small agency	project management software with client access; project management tool pricing for 10 users; project management software with time tracking

Notice the pattern: narrower versions, comparisons, costs, and the next question someone would ask. That's the coverage the rest of this guide is about.

How to Check Your Page's Query Fan Out Coverage

You can't see the exact searches an AI system runs, but you can check whether your page covers the likely ones:

1. Write down the main question your page answers.
2. List the likely related searches: Google's "People also ask" and related searches, the follow-up questions AI Mode suggests, and the questions your customers ask.
3. Ask an AI assistant to act as a query fan out generator: give it the question and ask for the related searches it would run. Treat the list as ideas, not as the real searches.
4. Check each one against your page: is it answered clearly, under a heading that names it? Mark it as answered, partly answered or missing.
5. Fill the gaps on the same page if they share the same intent, or link to another page that covers them. Only fill gaps that are useful to your readers; a likely related search isn't a reason on its own to write more.

RankAnalyze's Ranking Audit can help with step 4: it compares your page's topic coverage with the pages ranking for the same keyword and shows the angles you're missing.

A Quick Checklist

• Does your page answer the obvious follow-up questions someone researching this topic would have, not just the main question?
• Are related angles (comparisons, narrower use cases, common follow-ups) broken out under their own subheadings, not buried in one paragraph?
• Have you checked what's currently ranking and being cited for the related searches around your topic, not just your main keyword?
• Are you treating "ranks for the main keyword" and "covers the related searches well" as two different things to check, not one?

Frequently Asked Questions