What is prompt fan-out?

Prompt fan-out is what happens when an AI search engine takes one question and turns it into several smaller searches. You ask one thing. The system searches for many related things, then combines what it finds into one answer.

For anyone trying to be visible in AI answers, this matters. Your page may never match the question the buyer typed. It only has to match one of the sub-queries. The glossary entry on prompt fan-out has the short definition. This guide goes further.

How fan-out works

Take a buyer who types: "best accounting software for a small design studio."

A person would search that phrase and scan the results. An AI system often does something different. It may break the question into parts such as:

• accounting software for freelancers
• accounting software with invoicing
• pricing of popular accounting tools
• reviews of accounting software from small businesses
• accounting software integrations with payment tools

It runs those searches, reads what comes back, and writes one answer. The sources cited in that answer may come from any of the sub-queries, not from the original question.

The exact sub-queries are chosen by the system, and they can change from run to run. You usually cannot see all of them.

Who does it

Google has described this technique for AI Mode. Its documentation says AI Mode uses a "query fan-out" approach, issuing multiple related searches across subtopics and data sources. Google says AI Overviews can use the same technique.

Other assistants also search the web when a question needs fresh information. Microsoft's documentation for Copilot, for example, says it identifies terms where a search would improve the response and generates a search query. Whether a given assistant issues one query or many differs by product and by question, and it changes over time. Treat fan-out as a common pattern, not a fixed rule for every engine.

Why it matters for visibility

You compete on more than one query

A single question can lead to five or ten searches. Each is a chance to be found or missed. A page that ranks for a narrow sub-query can be pulled into an answer for a broad question.

Broad pages can lose to specific ones

A general page about "accounting software" may not match "invoicing features for freelancers" as well as a page written on exactly that. Specific, well-structured pages give the system something precise to pick up.

Your brand can be missing from part of the picture

If your product is strong on invoicing but no page or third-party source says so, the invoicing sub-query will not find you. You may be absent from the answer without knowing why.

Cited sources become clearer

The sources in an answer often reflect the sub-queries. Reading them tells you which angles the system explored, and which sites it trusted for each.

How to use fan-out to pick prompts to track

You cannot track every possible question. Fan-out gives you a way to choose a set that covers real ground.

Step 1: Start with the buyer questions

List the questions buyers ask before they pick a product like yours. Include category questions, comparisons and problem questions. Aim for 10 to 15 to begin with.

Step 2: Expand each one into sub-queries

For each question, ask what a system would need to know to answer it. Look for:

• Attributes: price, features, size of company it suits, integrations.
• Comparisons: you against named alternatives.
• Proof: reviews, case studies, independent write-ups.
• Use cases: the jobs people hire the product for.
• Constraints: country, language, budget, compliance.

You can do this by hand. Or use the free Prompt fan-out generator: enter a question and it suggests related sub-queries you can test.

Step 3: Turn the good ones into prompts

Not every sub-query makes a good tracking prompt. Keep the ones a real buyer might ask an assistant in natural language. Drop duplicates and anything too vague to give a stable answer.

Step 4: Group them

Keep prompts in groups, for example category, comparison, use case and pricing. When results come in, you can see which group is weak instead of staring at one blended number.

Step 5: Fix the list and track it

Keep the set stable so you can compare over time. Add new prompts as a separate group. Each prompt should be run repeatedly, since answers vary.

What to measure once you are tracking

For each prompt, record:

• Mention: is your brand named?
• Position: where in the answer does it appear?
• Sentiment: how are you described?
• Cited sources: which of your pages and which other domains are linked?
• Competitors: who else is named, and how often?

Share of voice ties these together: your mentions as a share of all brand mentions across the set.

Read the results by group. If you are named on category prompts but not on pricing prompts, the gap is in pricing content or in what third parties say about your pricing. That is a concrete task, which a blended score would hide.

A worked example

Suppose you sell scheduling software for clinics. Your seed question is "best appointment scheduling software for a small clinic."

Expanded, it might give these prompts:

| Group | Prompt | | --- | --- | | Category | best appointment scheduling software for small clinics | | Attribute | scheduling software with automatic patient reminders | | Comparison | your product vs a named competitor for clinics | | Constraint | scheduling software that meets health data rules in Germany | | Use case | how to reduce missed appointments at a clinic |

Five prompts from one question. Each can trigger its own sub-queries. Track all five and you will see whether you show up across the range, or only on the obvious one.

What to do with the findings

• Missing on an attribute: publish a clear page that covers it, in plain language.
• Missing on comparisons: write an honest comparison page. Check what third-party comparisons say about you.
• Cited sources are all third parties: work on being present and accurately described there.
• Wrong or dated descriptions: fix your own pages first, then the sources the answers cite.

None of this is a trick. It is ordinary content work, aimed by better information.

Limits to keep in mind

You cannot see every sub-query. Generators and manual expansion give you likely ones, not the exact list a system ran.

It changes. Systems are updated. A pattern that held last quarter may not hold now.

It is not a guarantee. Covering a sub-query well raises your chance of being used. It does not force a mention.

Tracking across engines

Fan-out affects AI Mode, AI Overviews and other assistants that search the web, so track across them. See our pages on Google AI Mode tracking and Google AI Overview tracking for the Google side.

Pineprompt tracks prompts daily across ChatGPT, Gemini, Perplexity, Claude, Google AI Overviews, Google AI Mode, Grok and Copilot, with country and language targeting. It reports mentions, position, sentiment, cited sources, competitors and share of voice. The methodology page explains how runs are recorded. Plans start at $99 a month with no free trial; see pricing.

The short version

One question becomes many searches. Your visibility depends on the sub-queries, not only the headline question. Use fan-out to widen your prompt list, group the prompts, keep the list fixed, and read results by group. Start with one buyer question and the generator today.