What is query fan-out, and why does it matter for AI search?

What is query fan-out?

Query fan-out is a retrieval pattern. Instead of sending a user's prompt to a search index as it is, the engine uses a language model to derive several narrower searches, runs them, and then writes one answer from what came back. Google used the term publicly in March 2025 when it launched AI Mode, describing related searches issued concurrently across subtopics and multiple data sources (Google, 2025).

breaking down your question into subtopics and issuing a multitude of queries simultaneously

A prompt like “Is a heat pump worth it for a 1970s house?” hides several information needs: installation cost, insulation requirements, running costs compared with gas, subsidies, noise. A fan-out system turns those needs into separate searches, and each search can return different pages. The final answer cites whichever sources covered each part best.

The idea is older than AI Mode. The self-ask method by Press et al. showed that a model asking itself follow-up questions, answered with a search engine, is more accurate on multi-step questions (Findings of EMNLP 2023). Microsoft described a related approach for Bing's deep search in 2023: the query is rewritten on the user's behalf and the variations are searched as well (Bing, 2023).

Which AI search engines use query fan-out?

Every major engine that documents its search behavior describes some form of multiple, model-generated searches per request. The details differ, and so does how much of it you can see. For how Google's two AI surfaces differ in general, see AI Mode vs. AI Overviews.

Two nuances matter. Google's wording is “may use”, so not every AI Overview necessarily involves a fan-out. And the developer APIs describe how a model searches when you call it; the consumer apps can behave differently. OpenAI's help center, for example, says ChatGPT may use saved memories when it rewrites a search query.

What do Google's patents say about fan-out, and what don't they prove?

Patents are often quoted as evidence of how AI Mode works. They show what Google has filed, not what runs in production. Three are cited frequently:

• US11769017B1, “Generative summaries for search results” (granted 2023): describes an LLM summary built not only from documents responsive to the query, but also from documents responsive to related queries, recent queries and implied queries (Google Patents).
• US20240289407A1, “Search with stateful chat” (application published 2024): claim 1 covers generating synthetic queries from model output and selecting search result documents that respond to the original query and to those synthetic queries (Google Patents).
• WO2024064249A1, “Systems and methods for prompt-based query generation for diverse retrieval” (published 2024): despite the title, this one uses an LLM to generate synthetic query-document pairs for training a retrieval model. It does not describe fan-out at answer time (Google Patents).

The first two describe the same shape as Google's blog posts: derive additional queries, retrieve for all of them, summarize. None of them tells you how many sub-queries AI Mode runs, how results are weighted, or which parts are live.

Why does query fan-out matter for your content?

Retrieval happens per sub-query. If an engine searches “heat pump running costs vs gas boiler” as one of its fan-outs, the pages that rank for that sub-query become candidates for the answer, even if they never ranked for the prompt the user typed. Google says its models identify more supporting pages while a response is generated, which lets it show a wider and more diverse set of links than a classic web search (Search Central).

Subtopic coverage becomes visible. A fan-out list is the engine telling you which facts it needed. If the sub-queries keep asking about pricing, compatibility and alternatives, a page that only answers the head question leaves those parts for other sources to fill.

Planning shifts from single keywords to question sets. Keyword research tells you what people type. Fan-outs show what an engine searches on their behalf, and those searches are often longer and more specific. Google notes that people in its AI experiences ask longer, more specific questions and follow-up questions (Search Central Blog, 2025).

What are the limits of fan-out data?

• Fan-outs vary from run to run. In every documented system the model decides whether and what to search, and ChatGPT may send further queries depending on what the first results contain. The same prompt can produce different sub-queries on different days, so treat one observation as an anecdote and look for sub-queries that recur.
• Not all fan-outs are visible. Google does not publish the sub-queries behind AI Mode or AI Overviews, and Search Console includes AI feature traffic in the overall “Web” search type rather than per sub-query. OpenAI's API documentation says a search action usually, but not always, includes the queries that were searched.
• Engines differ. ChatGPT can send rewritten queries to search partners, Gemini grounds on Google Search, Claude uses Anthropic's web search tool. Each has its own index and ranking, so a sub-query that surfaces your page in one engine may not in another.
• API answers are a proxy. Fan-outs collected through developer APIs show how a model searches under API conditions. The consumer app can differ through memory, location or model choice.
• Gemini is not AI Mode. The queries the Gemini API reports for grounded answers are Gemini's own, not a view into AI Mode's fan-out, even though both use Google Search.

What this means for you: how should you plan content around fan-outs?

1. Collect fan-outs repeatedly from engines that expose them, across several runs and engines, for the prompts that matter commercially.
2. Normalize and count. Group near-identical sub-queries and rank them by how often they recur across runs, engines and prompts. Recurrence tells you more than any single list.
3. Cluster into subtopics such as pricing, comparisons, compatibility, how-to and freshness. Each cluster is a candidate section, FAQ entry or standalone page.
4. Map clusters to existing URLs. Decide which page should answer each cluster, and create a new page only when no existing page can carry the subtopic without losing focus.
5. Write passages that answer the sub-query directly. Put the fact, number or recommendation in the first sentence of the section, with a date and a source where it matters. Google says AI features have no special requirements beyond being indexed and eligible for a snippet, so this is ordinary good content, organized by the questions engines ask. More techniques are in GEO techniques.
6. Check the basics: crawlable, indexed, important content in text form (see AI crawler readability).
7. Re-measure citations for the prompt after the change, per engine, not only rankings for the head term.

What does a fan-out analysis look like in practice?

Example: ExampleTrack is a hypothetical company that sells time-tracking software to small agencies. All numbers below are illustrative, not measured data.

1. The team tracks the prompt “What's the best time tracking tool for a 10-person design agency?” on ChatGPT, Claude, Gemini and Perplexity every day for four weeks.
2. Over that period the engines run about 40 distinct sub-queries (illustrative). After grouping near-duplicates, five recur often (table below).
3. The team clusters them: pricing, project-management integrations, invoicing, freshness and one direct comparison.
4. They map the clusters to their site: prices appear only inside an image, integrations and invoicing have no page, the best-of article is from 2023, and the comparison exists only as a sales deck.
5. They put per-seat prices in plain text with a “last updated” date, add an integrations and invoicing section with a short FAQ, refresh the best-of article and publish an honest comparison page.
6. After another four weeks they compare, per engine, how often their pages are cited for the prompt against the first period.

The example shows the order of work: observe repeatedly, count, cluster, map to pages, fix the passages, measure citations again. None of this guarantees a citation; it removes the obvious reasons for not getting one.

How can you capture fan-out queries with AutoSEO?

AutoSEO records fan-outs as part of AI visibility tracking, but only where the provider returns them. As of September 2026 the code captures sub-queries for ChatGPT, Claude, Gemini and Perplexity when answers come through DataForSEO (including its ChatGPT app scraper), and through the direct APIs of OpenAI, Anthropic, Gemini, Perplexity's Agent API, xAI (Grok), Mistral, Meta AI and Kimi when their responses include search queries. Collection through DataForSEO returns no fan-outs for Google AI Overviews, Google AI Mode and Microsoft Copilot. If these engines run on AutoSEO's simulated fallback instead (a search-grounded model imitating the engine, labelled Simulated), the stored queries are that model's own searches, not Google's or Microsoft's. DeepSeek's API has no web search, and answers from the local agent carry no fan-outs either.

• Query Fanouts page: each answer's sub-queries are stored with prompt, engine and date. The page groups them across answers and shows frequency, engines, originating prompts and first and last seen dates, with search, a period filter and CSV download (query fan-out analysis).
• Per answer: the answer view lists the Fan-out queries next to the answer text and its citations.
• Ad-hoc prompts: Prompt Explorer runs a prompt live on ChatGPT, Claude, Gemini or Perplexity via DataForSEO and shows the sub-queries under Related queries the model considered (prompt research).
• Content plans: for tracked prompts with at least two answers, where engines cite at least two other pages and none of yours, AutoSEO creates a content-gap task whose suggested outline uses that prompt's most frequent fan-outs as headings. You can draft the page with the content tools.
• Agents, API and reports: the MCP tool get_query_fanouts and the REST endpoint GET /api/v1/projects/{projectId}/fanouts return the same grouped data (default period: 90 days) for your own agents and scripts (MCP server), and reports can include a Query fan-outs block.

To try this on your own prompts, you can self-host AutoSEO for free or use AutoSEO Cloud for $50 per month.