Query Fan-Out Explained: Content Strategy for AI Search

Query fan-out is the step where an AI search engine turns one question into several smaller searches. A buyer asks "who is a reliable packaging supplier for food brands in Texas?". The engine may also look up certifications, minimum orders, lead times, reviews and other suppliers. It reads the results of all those searches, then writes one answer and picks a few pages to cite. A citation is a link the answer shows as its source.

The name comes from the shape: one query fans out into many. The buyer never sees the extra searches. They only see the final answer. That is why a page can rank well for the exact question and still be left out. The engine chose its sources while answering questions the buyer did not type.

Google is the clearest. Google's AI features guide says AI Overviews and AI Mode may use a "query fan-out" technique, "issuing multiple related searches across subtopics and data sources". Google's AI Mode announcement of 2025-05-20 adds that AI Mode breaks a question into subtopics and issues "a multitude of queries simultaneously", and that Deep Search, its research mode, "can issue hundreds of searches". Our Google AI search hub covers the rest of what Google says.

OpenAI does not use the term, but its developer docs show the same pattern. In OpenAI's web search guide, each search the model runs is logged as a web_search_call, which usually lists the queries it searched. OpenAI also says a reasoning model "can refine its searches as it reads results". Perplexity and other market players do not publish a fan-out description we could check, so we do not make claims about them. Every other ChatGPT guide is listed on our ChatGPT SEO hub.

Classic SEO (search engine optimization) aims one page at one keyword. Fan-out means the engine is also looking for the answers around that keyword. If your page covers the main question but says nothing about price range, delivery area or who it suits, the engine may take those answers from a directory, a review site or another market player. That page then earns the citation.

Fan-out does not need special code. Google states that "there are no additional technical requirements" and that "there's also no special schema.org structured data that you need to add". A page only has to be indexed and able to show with a snippet. So the work is mostly editorial: decide which sub-questions matter, and make sure a clear answer to each one exists on a page the engines can reach.

List the questions a buyer who does not know you yet would ask an AI assistant. Use their words, not your product names. Group them by intent: comparing options, checking a price, finding a supplier near them, or solving a problem. Keep the list short enough that you can review every answer by hand.

If you already track AI visibility, reuse the same list. Our guide to prompt set design for visibility tracking explains how to shape and freeze it. To find candidate questions from your own search and sales data, see how to find the prompts your customers ask ChatGPT.

For each prompt, write down the sub-questions an engine would need to answer it well. Think in facets: what it is, what it costs, who it suits, how it compares, what proof exists, where and how fast it is delivered. A facet is one angle of the topic that needs its own answer.

The free Leadee Query Fan-Out tool does this for one prompt at a time and tags each sub-question with its intent. It is a model built from Leadee's own rules, not a copy of the searches Google or ChatGPT ran. Treat its list as a checklist to review, not as a record. The ChatGPT fan-out simulator gives a second view of the same prompt.

Make a simple table: prompt, sub-question, the page that answers it, and how well it answers (clear, partial or missing). Give each sub-question one owner page. Two pages that answer the same thing half-way compete with each other; one page that answers it fully is easier to quote.

Not every sub-question deserves a new page. Price, delivery area and minimum order often fit as short sections on an existing service or product page. A comparison or a full how-to usually needs its own page. Only create a page when the answer would be too long or too different to sit inside an existing one.

Put the sub-question in a heading, and answer it in the first sentence or two below it. Keep facts specific and checkable: product names, service areas, standards you meet, how you price. Explain any jargon in plain words the first time you use it. An engine that reads the section on its own should still get a complete answer.

Keep the important facts in the page text, not only in images or PDFs; Google lists "making sure that important content is available in textual form" among its good practices for AI features. Our guide on how to write pages ChatGPT can quote shows the passage format in detail, and how to get cited by ChatGPT covers the rest of the citation picture.

Some sub-questions are answered by other sites by design. "Is this supplier reliable?" leads to reviews and directories. "Who else does this?" leads to lists and comparisons. Your own page cannot be the only source for those, so make sure your listings, profiles and reviews on the sites engines cite are complete and match your own facts.

Our guide to how AI chooses which suppliers to recommend explains which outside sources matter for B2B buyers. For online stores, how AI search chooses which stores to cite gives the retail version.

A sub-question answer only counts if engines can fetch it. Check that your robots.txt file, which tells crawlers what they may read, lets the search bots in; our guide to AI crawlers and robots.txt lists the exact lines. Then confirm the page is indexed in Google, since Google requires that for a supporting link.

Finally, measure the main prompts, not the sub-questions: the buyer only sees the final answer. Run each prompt several times, record whether you are named and cited, and compare periods. Our guide on tracking brand mentions in ChatGPT and Perplexity gives the method, and the Visibility Tracker runs it for you.

Example. A contract manufacturer of metal parts wants to appear for "best CNC machining supplier for medical device prototypes in the US". A fan-out review might list the sub-questions below. Next to each one, the team writes the page that should answer it, or "missing".

Leadee is an AI-native platform that stands on the shoulders of giants: it reads what Google, OpenAI and other engines publish, and it asks the engines through their official APIs rather than scraping consumer chat apps. For fan-out, that means three tools that work together.

Leadee expands the prompts, checks your pages against the sub-questions and measures the answers. Your team decides which sub-questions matter for your buyers, writes or approves the content, and keeps outside listings accurate. Leadee never changes your website on its own.

If you want experts to build the fan-out map and turn it into a content plan, start with the AI Visibility Audit. If you want the content written and published for you as well, see Managed GEO. Plans and prices are on the pricing page, and the methodology page explains how Leadee measures.