Free tool
AI Query Fan Out Analysis
One prompt becomes several hidden searches. This shows the searches your prompt actually ran.
How it works
What is the AI Query Fan Out Analysis?
The AI Query Fan Out Analysis is a free tool that simulates the background searches an AI answer engine might run for one prompt. Pick ChatGPT Search, Google AI-Mode or Perplexity Pro, and it returns four sub-queries per engine, each labelled with its intent and a one-line reason. Use it to see which related questions a page has to cover.
How to use the AI Query Fan Out Analysis
- Type a prompt the way a person would ask it in a chat, such as best technical SEO tool for a small team, rather than a keyword.
- Under Engines to simulate, pick one or more of ChatGPT Search, Google AI-Mode and Perplexity Pro. ChatGPT Search is picked by default.
- Press Analyze fan out, then Sign up free to unlock to see the result.
- Read each engine's Expansion: four sub-queries, each with an intent badge and a one-line reason.
- Press Export to CSV to take the sub-queries, their types and reasons into a spreadsheet.
What is the query fan-out technique?
Query fan-out is when an AI search system splits one prompt into several related searches, runs them, and writes one answer from everything they return. Google has described AI Mode as working this way.
It explains a result that otherwise makes no sense: a page that ranks for the exact phrase a person typed and is still missing from the AI answer to that phrase. The searches that ran were for the sub-questions, not for the phrase.
Why do ChatGPT, Google AI Mode and Perplexity get different sub-queries?
Partly because they search different places, and partly because this tool briefs each one differently. ChatGPT Search is simulated as browsing the web through Bing for current context, Google AI-Mode as grounding its answer in Google Search, and Perplexity Pro as a deep research pass that cites authoritative sources.
So the same prompt comes back as a different set of sub-queries for each engine. Simulate the engines your buyers use and compare the sets side by side.
How do you optimize content for query fan-out?
Treat each sub-query as a question one of your pages has to answer in its own text. The common failure is a topic split too finely: four sub-queries answered across four separate pages, so no single URL covers the prompt, and a competitor page that covers all four is the easier source to use.
No engine publishes its internal searches, so a simulation is the practical way to see the decomposition. Use it as a map of the shape of a question.
Questions
- What does query fan-out mean for SEO?
- Query fan-out means the unit of SEO for AI answers is the sub-question, not only the phrase a person typed. A page gets used when it answers one of the searches the engine actually runs, so coverage of the related questions matters as much as ranking for the main keyword.
- What is an example of query fan-out?
- A prompt such as best CRM for a small agency might fan out into searches for CRM pricing for small teams, CRMs with client portals and reviews of agency CRMs. Each is searched on its own, and the answer is written from what they all return.
- Does ChatGPT use query fan-out?
- Yes, in practice. ChatGPT search can run more than one search for a single prompt before it answers. OpenAI does not publish those searches, so the ChatGPT Search mode in this tool simulates them rather than reading them.
- Does Google AI Mode use query fan-out?
- Yes. Google has said AI Mode uses a query fan-out technique, breaking a question into subtopics and searching for each of them at once. The Google AI-Mode option here simulates that decomposition for your prompt.
- How do I find fan-out queries?
- You find fan-out queries by simulating them, because no engine publishes the searches it runs. Enter a prompt in this tool, pick the engines, and read the sub-queries each would plausibly run. Treat them as a plan for coverage, not as a log.
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