GeoWhen someone asks an AI assistant a question about your business, the assistant often does not search for that question. It searches for a dozen related ones, then writes one answer. If your page only answers the headline question, you may be missing from most of those searches.
What is query fan-out?
Query fan-out is when an AI search system splits one prompt into several background searches on related subtopics, then combines what it finds into a single answer. Google says AI Overviews and AI Mode may use it. Perplexity, Gemini, ChatGPT, and Copilot work in similar ways, though not all of them use the term.
Google's Head of Search described the idea this way: "Search recognizes when a question needs advanced reasoning. It calls on our custom version of Gemini to break the question into different subtopics." Semrush estimates that one complex prompt can trigger eight to 20 or more background searches. That is an estimate. Google has not published an official count.
Why does fan-out change how I should write a page?
The page that gets cited is the one that answers the sub-questions, not only the headline question. Search Engine Land puts it plainly: "Content that only answers one narrow angle may still be accurate, but it's easier to replace as fan-out expands." Accuracy gets you in the room. Coverage keeps you there.
Take a dental practice with a page titled "How much does a root canal cost?" A person asking that may get an answer assembled from searches like cost without insurance, whether it hurts, how long it takes, and what the alternatives are. Those four are my guess at the shape of it, not observed data. A page that covers all four has four chances to be pulled in. A page that lists a price range and stops has one.
How do I find the sub-questions my page should answer?
Start with the customer's main question and write down what they would ask next. Search Engine Land's guide lists eight angles, including paraphrases, follow-up questions, specifications, and clarifications. Semrush lists six types, including comparisons, recency, and next-step questions. You do not need the taxonomy. You need the list.
Good sources are the ones you already have: emails and phone calls from customers, the People Also Ask box on Google, and the questions your front desk answers every week. You can also ask an AI assistant what it would need to know to answer your topic well. It will not show you its real sub-queries, but the guesses are usually reasonable.
How should I structure the page?
Put every sub-question on one page, each under its own question heading, with a direct 40 to 60 word answer first and detail after. Search Engine Land's advice is that fan-out optimization prioritizes "semantic completeness" over URL granularity. In practice that means fewer thin pages and more thorough ones.
Use a table when the answer is a set of facts, such as prices by service or hours by location. Name things explicitly: the service, the city, the price, the date. Add FAQ schema that matches the questions on the page. This post follows its own advice: each heading is a question, and each answer sits right under it.
Does optimizing for query fan-out actually work?
There is some evidence, and it is thin. Semrush ran a case study on its own site. After optimizing four blog articles for relevant fan-out queries, its citation count went from two to around five within a month. That is a vendor measuring its own content with a very small sample, so treat it as a direction and not a guarantee.
The bigger limitation is visibility. Nobody outside the AI companies can see the real sub-queries, so every list you build is an informed guess. The honest way to test it is to track whether your pages get cited for the prompts your customers use. Keep a list of 20 to 50 of those prompts and check them every week.
Does anything off my own site matter for fan-out?
Yes. Some of the background searches will land on directories, review sites, and press, not on your website. Semrush's advice includes building presence through directories and PR. For a local business that mostly means accurate listings and real reviews, because those are the pages an assistant may pull when a sub-query asks what other customers say.
A fan-out-ready page on a site AI crawlers cannot reach, with no schema and no outside mentions, will still lose. The page is one piece.
Think you can hit an A?
Here's a sample GeoRoar scorecard. Yours takes about 30 seconds.
This is a sample. Your real scorecard is free, no sign-up.
Run your free GEO audit