What’s question fan-out? Tips on how to discover & optimize for subqueries

A search question was once simply that: one question, one set of outcomes. Now, one complicated AI search question can set off an estimated eight to twenty or extra background searches (Google hasn’t printed actual counts), every producing its personal outcomes. These outcomes are scored for relevance, with the strongest ones throughout all of the sub-searches being synthesized into one complete reply.

That is question fan-out in a nutshell, and it’s how AI search techniques enhance their solutions’ usefulness. It’s additionally a key consider deciding which pages these techniques use to generate solutions: pages that tackle extra background subqueries are probably extra related to the immediate, making AI techniques extra prone to reference and cite them. Even when they don’t rank #1 for any key phrases.

Under, we’ll cowl how question fan-out works, methods to discover the subqueries related to your model, and methods to optimize for them.

What’s question fan-out?

Question fan-out is an AI search system course of that splits a person question into a number of subqueries (also called “fan-out queries”), collects data for every subquery, after which merges related data right into a single response.

Google popularized the time period when introducing Google AI Mode, a conversational AI interface obtainable inside Google Search.

In her Google I/O 2025 keynote speech, Head of Search Elizabeth Reid mentioned: “AI Mode isn’t simply supplying you with data — it’s bringing a complete new stage of intelligence to go looking. What makes this doable is one thing we name our question fan-out method.”

“Now, below the hood, Search acknowledges when a query wants superior reasoning. It calls on our customized model of Gemini to interrupt the query into completely different subtopics, and it points a large number of queries concurrently in your behalf.”

AI search techniques use question fan-out to reinforce their responses. In reality, Google’s official search documentation mentions question fan-out as a core method for retrieving content material from its search index to generate AI solutions.

How question fan-out works

Question fan-out works by figuring out a immediate’s intents, retrieving content material that fulfills them, after which synthesizing a solution from essentially the most related outcomes.

Broadly, this mannequin contains 5 elements:

• Evaluation of the immediate to know its intents, or what the person is searching for. For instance, the immediate “What’s the most effective laptop computer for a school pupil who wants lengthy battery life and does some video enhancing?” signifies that the person is searching for a laptop computer that’s appropriate for school use, has an extended battery life, and may assist video enhancing work. AI thus must generate a solution that fulfills all three intents.
• Decomposition, or breaking down, of the immediate into a number of subqueries that tackle the recognized intents. AI would possibly cut up our instance immediate above into subqueries like “greatest laptops for school college students,” “laptops with lengthy battery life,” and “video enhancing laptop computer necessities.”
• Retrieval, the place the AI system searches sources like proprietary and search engine indexes for content material that addresses any of the immediate’s subqueries. The system may additionally subsequently conduct extra searches based mostly on what it discovers from preliminary outcomes.
• Scoring of all retrieved content material based mostly on how effectively it addresses every subquery, to establish and rank the outcomes most related to the immediate. 
• Synthesis, the place the AI system makes use of the highest-scoring content material to generate its reply

The implication? Pages that tackle the intents behind a number of subqueries usually tend to be referenced and cited by AI. Addressing only one total intent, corresponding to 1 goal key phrase, is now not sufficient.

Sorts of fan-out queries

Sorts of fan-out queries embody reformulation, implicit, comparative, recency, contextual variation, and next-step. Right here’s what they imply and question fan-out examples for every.

One caveat about these queries: they’re artificial and probabilistic. AI techniques generate them on the fly, they shift from one run to the following, and most carry little or no search quantity of their very own. Deal with them as intent indicators to cowl, not actual phrases to focus on.

All these fan-out question varieties level to particular person intents, like wanting the most recent data (recency queries) or data tailor-made to their scenario (contextual variation queries). Being conscious of those intents and fulfilling them in your content material are key for optimizing for question fan-out in AI.

Why optimizing for question fan-out issues for search engine and AI visibility

Optimizing for question fan-out issues for search engine and AI visibility as a result of manufacturers whose pages tackle customers’ intents extra totally could achieve larger search engine visibility and extra AI mentions and citations.

Manufacturers that cowl related subqueries stand to characteristic in Google’s AI Overviews, which can seem on the high of the search engine’s outcomes pages. Our AI search site visitors research discovered this visibility more and more shapes the place discovery begins. Similar to how this Google AI Overview prominently recommends Samsung and Apple’s telephones in response to the search question “greatest cellphone for live performance movies”:

The AI Overview recommends these manufacturers, citing web sites like Digital Digicam World in assist, though none of their pages rank #1 on the search outcomes.

AI search platforms may additionally reference and cite a model’s pages extra usually when their content material constantly seems related to fan-out queries.

Our expertise updating 4 weblog articles to focus on related fan-out queries factors on this path, although the impact was directional moderately than assured. Inside a month, these articles’ quotation counts for our tracked prompts greater than doubled, from two to round 5. The climb was unstable, spiking as excessive as 9 earlier than settling, and model mentions really dipped over the identical interval. We deal with this as encouraging early proof, not a settled consequence.

Tips on how to discover fan-out queries

To search out fan-out queries your content material doesn’t cowl but, use instruments that run your goal prompts in AI techniques to floor these prompts’ doable subqueries. We share two of those instruments under.

Upon getting a listing of subqueries, strike out these your content material already addresses, so you possibly can concentrate on optimizing for the remainder.

AI techniques generate responses probabilistically. So, the identical immediate can produce completely different fan-out queries every time you run it. We propose figuring out developments in subquery intents moderately than making an attempt to nail down particular subquery wording.

1. Semrush

Semrush is a search visibility platform that helps manufacturers observe and enhance their prominence in search engine outcomes and AI solutions. The platform’s AI Visibility Toolkit approximates the subqueries that AI techniques could generate in your goal prompts, whereas Enterprise AIO reveals Google’s precise fan-out queries at scale.

To search out fan-out queries with AI Visibility Toolkit, enter your goal subject into the Immediate Analysis report and click on “Analyze.”

The “Matters” tab of the report’s “Associated subjects” part reveals subjects associated to your immediate. Click on any subject to view its related sub-prompts, which AI techniques may additionally use as fan-out queries.

To view Google’s precise fan-out queries for prompts, use Enterprise AIO’s Question Fan-Out Evaluation AI Automations.

2. Question Fan-Out Software

Queryfanout.io is a free device that identifies subqueries by replicating Google’s question fan-out course of in your goal immediate.

Different choices for locating fan-out queries embody ChatGPT Question Fan-Out Software, a free question fan-out generator for Chrome browsers.

Tips on how to optimize for question fan-out

When you’ve recognized related fan-out queries, use these steps to optimize your pages for them:

1. Determine core subjects

Determine core subjects to construct your AI visibility round, so that you focus your question fan-out optimization efforts extra successfully.

Begin with subjects immediately associated to your model and what you supply. Doing this helps you:

• Management how AI techniques painting your model of their solutions
• Present up throughout buyer journey phases the place visibility and affect matter most
• Leverage your authority because you’re an knowledgeable in these subjects

Determine your model’s most essential subjects with the Questions report in Semrush’s AI Visibility Toolkit. To do that, enter your area into the report. Then, view the subjects within the “Subject Distribution” part.

Subsequent, view the report’s “Intent by Subject” part to be taught the dominant intent for every subject. This data is useful for mapping the subjects to buyer journey phases.

After figuring out brand-related subjects, increase your analysis to cowl associated subjects that align together with your model’s experience. Prioritize these associated subjects based mostly on what you are promoting objectives and viewers pursuits.

For instance, at Semrush, we publish content material that covers not simply our search visibility instruments, but additionally broader digital advertising subjects.

2. Plan subject clusters

Plan the subject clusters you’ll publish content material round to cowl your recognized fan-out queries.

Subject clusters are teams of interlinked webpages that collectively cowl a core subject in depth. They’re made up of a central pillar web page, which gives a broad overview of the core subject, and a number of other cluster pages, which cowl related subtopics in a number of subsections, together with ones that tackle fan-out queries.

Subject clustering can encourage AI techniques to prioritize mentioning and citing your content material. It builds topical authority whereas immediately addressing the subqueries these techniques could generate throughout question fan-out.

You possibly can create a thoughts map like this to plan your subject clusters:

For those who need assistance figuring out subtopics, use Semrush’s Key phrase Technique Builder. Enter your core subject (like a subject you discovered with the AI Visibility Toolkit), and the device teams the associated queries into subject clusters, with a urged web page for every one.

The place related, use these clusters as your cluster pages’ subjects, and the queries grouped below every one as subsections inside these pages.

Then, map your recognized fan-out queries as new or present cluster web page subsections. In case your fan-out queries don’t match inside any present cluster web page, begin new ones for them.

3. Write NLP-friendly content material

Write pure language processing (NLP)-friendly content material in your subject cluster pages in order that AI techniques can higher perceive how their content material fulfills related fan-out question intents.

NLP is a department of synthetic intelligence that helps computer systems course of, perceive, and generate textual content in human language. Writing in methods like these can enhance how AI techniques perceive your content material:

• Write in chunks. Chunks are self-contained, significant sections of content material that may stand on their very own, letting AI techniques course of, retrieve, and summarize them extra simply. Write in full sentences, and restate context the place useful.
• Present definitions. Once you introduce a brand new idea, present a transparent and direct definition of it. Doing this helps AI techniques perceive what you’re speaking about, particularly after they use a fan-out question like “[topic] definition” to get a definition.
• Construction content material successfully. Add descriptive subheadings to interrupt your content material into sections, and use heading tags to point out their hierarchy. Structuring your content material like this helps AI techniques establish content material associated to extremely particular subqueries. You may also use tables and lists to create simply parsable data. Our information to optimizing content material for AI search engines like google and yahoo goes deeper on every of those.
• Use clear language. Keep away from jargon, overly complicated sentence buildings, and pointless fluff. Writing clearly makes it simpler for AI techniques to know your content material and extract precious data.

4. Apply schema markup

Apply schema markup to your pages to assist AI techniques parse and extract their content material for related subqueries.

Schema markup is a technique of formatting knowledge in your pages’ HTML to sign to machines, like search engines like google and yahoo and AI techniques, the varieties of content material these pages comprise.

For instance, you need to use Product schema markup so as to add machine-readable labels to a product’s identify and dimension:

With these labels, AI techniques can higher establish your product’s identify and dimension for addressing subqueries about both attribute.

Take a look at our information to schema markup to be taught extra concerning the obtainable schema markup varieties and methods to implement them in your pages.

5. Construct your off-site presence

Construct your model’s presence on third-party websites, as a result of AI techniques search the broader internet for solutions to fan-out queries, not simply your web site. A bigger off-site footprint offers these techniques extra surfaces to find your model’s relevance to subqueries, and extra probabilities to say you in responses.

Enhance your off-site presence by taking steps like these:

• Arrange listing listings: Create a profile in your model on directories related to your business. Examples of directories are G2 for software program companies and Yelp for eating places. For those who run a neighborhood enterprise, declare your Google Enterprise Profile as effectively, because it’s a key data supply for AI techniques.
• Run digital public relations campaigns: Safe protection of your newest achievements, merchandise, or findings, on respected business publications. This protection feeds AI techniques recent solutions to subqueries about your model’s credibility and newest information.
• Take part in group boards: Present useful responses on Reddit, Quora, and different boards, particularly the place discussions relate to widespread subquery intents, to encourage AI techniques to reference your enter in solutions.

6. Optimize for business queries

Optimize for business queries by addressing the fan-out queries AI techniques generate about your choices’ traits, like their coloration, mannequin, worth, free plan availability, or working hours, as related. Doing this helps AI techniques advocate your choices in response to related prompts extra usually.

Techniques for optimizing for business fan-out queries embody:

• Handle potential fan-out queries immediately by including related content material to your product and repair pages. For instance, in case you’ve recognized “[product name] refund coverage” as a possible fan-out question, you possibly can add a query like “What’s the refund coverage for [product name]?” to your product itemizing’s FAQs part, adopted by your reply to it.
• Publish comparability content material to form the AI narrative about how your choices stack up in opposition to rivals’ in response to fan-out queries like “[your product] vs [competing product] overview”
• Get extra person evaluations, as they assist corroborate details about your choices, like whether or not they’re true to dimension. Optimistic overview language can even affect AI techniques to explain your choices extra favorably. These results matter most when customers are actively researching whether or not your providing meets their wants.

7. Test your subquery protection

Test your subquery protection to establish how effectively your content material addresses your goal fan-out queries’ intents, so you possibly can take steps to enhance protection.

Record your pages, after which map them in opposition to the subject clusters and fan-out queries you’ve recognized earlier. Flag fan-out queries that your pages at the moment don’t tackle.

Subsequent, test the standard of content material on pages that do tackle fan-out queries. Your content material’s high quality impacts whether or not AI techniques use your pages to generate solutions, so flag pages whose content material seems skinny or irrelevant to your goal subqueries.

Lastly, plug your recognized gaps. Create new pages for fan-out queries you lack protection for. Additionally, replace present pages to offer extra direct, related solutions to subqueries that want stronger protection.

Repeat this protection audit, and seek for new fan-out queries, each quarter. Common upkeep will aid you choose up and fill new content material gaps as your viewers’s preferences evolve.

Bonus: Mini case research

As a bonus, right here’s a mini case research of how Stripe’s advertising efforts show many rules of question fan-out optimization.

The model’s web site has options pages tailor-made to completely different enterprise phases, enterprise fashions, and use instances. In flip, these pages have subsections that present direct, detailed data on related subtopics.

This detailed and diversified data probably helps AI techniques acknowledge Stripe’s relevance to varied intents and extract helpful data for fan-out queries.

The Stripe web site additionally covers related subjects by sources like its weblog, buyer tales, assist middle, and newsroom.

Within the information under, Stripe makes use of clear structuring to interrupt down a posh subject. And gives direct, easy-to-understand explanations all through.

Past its web site, Stripe usually options in press protection from respected business publications and information websites, which helps increase its visibility on the broader internet.

Stripe’s AI search visibility considerably outperforms rivals’, in line with knowledge from Semrush’s AI Visibility Toolkit. A number of elements probably clarify this, however the breadth and depth of the model’s on-site content material, plus its intensive off-site presence, may have performed an particularly essential function.

Measure your fan-out protection

Measure your fan-out protection with a device like Semrush’s AI Visibility Toolkit, which studies in your model’s visibility and narrative in AI techniques.

Enter your area into the device, and its Narrative Drivers report will present your share of voice for non-branded queries throughout AI search platforms. In different phrases, how usually these platforms point out you versus (or alongside) your rivals for queries that don’t comprise names of manufacturers.

You possibly can even see in case your model is talked about first, second, or additional down in varied AI techniques’ responses to particular prompts.

The “Key Sentiment Drivers” part of the device’s Notion report gives perception into your model’s portrayal in AI responses, too.

Work to emphasise strengths and mitigate weaknesses, so that you generate extra constructive protection in AI responses. And finally entice extra clients.

FAQs

Does question fan-out apply to ChatGPT and different LLMs, or simply Google AI Mode?

Question fan-out applies to ChatGPT and plenty of different LLMs, not simply Google AI Mode. Google coined the time period to explain its data retrieval method for AI Mode, and the business has since adopted “question fan-out” as a normal time period for the equal course of in LLMs like ChatGPT and Claude. Not one of the firms that develop these LLMs have shared their very own identify for it.

What number of subqueries does AI run per immediate?

The variety of subqueries AI runs per immediate depends upon the immediate’s complexity and the way highly effective the AI’s mannequin is. An AI system would possibly run only one subquery for a easy immediate, however hearth off wherever from 8 to twenty+ subqueries for a posh one, particularly in case you allow the system’s deep analysis mode.

Are fan-out queries the identical as long-tail key phrases?

No, fan-out queries aren’t the identical as long-tail key phrases. Whereas each could look related, long-tail key phrases are the product of actual human search habits, being phrases that individuals have typed right into a search bar. In distinction, fan-out queries are artificial. AI techniques generate them on the fly to retrieve a bigger variety of related outcomes for constructing fuller solutions.

How do I discover the fan-out queries for a subject?

To search out fan-out queries for a subject, use instruments that may run prompts associated to it, after which floor their doable subqueries. For instance, Semrush’s AI Visibility Toolkit approximates the subqueries that AI techniques could generate in your goal prompts, whereas Enterprise AIO reveals Google’s precise fan-out queries at scale.

Does schema markup assist with question fan-out?

Schema markup helps question fan-out by including machine-readable labels to knowledge in your pages, which helps AI techniques parse and extract it precisely. It is not a quotation lever by itself. Sorts of knowledge you possibly can add schema markup to incorporate product and repair data, and often requested questions and solutions.

How does question fan-out have an effect on ecommerce and product pages?

Question fan-out impacts ecommerce and product pages by figuring out the pages AI techniques floor after they break business queries into subqueries about specifics like coloration, mannequin, and worth. These techniques usually tend to reference, and generate solutions utilizing, pages that tackle these specifics clearly.