A hunt query utilized to beryllium conscionable that: 1 query, 1 group of results. Now, 1 analyzable AI hunt query tin trigger an estimated 8 to 20 aliases much inheritance searches (Google hasn't published nonstop counts), each producing its ain results. These results are scored for relevance, pinch the strongest ones crossed each the sub-searches being synthesized into 1 broad answer.
This is query fan-out successful a nutshell, and it’s really AI hunt systems amended their answers’ usefulness. It’s besides a cardinal facet successful deciding which pages these systems usage to make answers: pages that reside much inheritance subqueries are apt much applicable to the prompt, making AI systems much apt to reference and mention them. Even if they don’t rank #1 for immoderate keywords.
Below, we'll screen really query fan-out works, really to find the subqueries applicable to your brand, and really to optimize for them.
What is query fan-out?
Query fan-out is an AI hunt strategy process that splits a personification query into aggregate subqueries (also known arsenic “fan-out queries”), collects accusation for each subquery, and past merges applicable accusation into a azygous response.
Google popularized the word erstwhile introducing Google AI Mode, a conversational AI interface disposable wrong Google Search.
In her Google I/O 2025 keynote speech, Head of Search Elizabeth Reid said: “AI Mode isn’t conscionable giving you accusation — it’s bringing a full caller level of intelligence to search. What makes this imaginable is thing we telephone our query fan-out technique.”
“Now, nether the hood, Search recognizes erstwhile a mobility needs precocious reasoning. It calls connected our civilization type of Gemini to break the mobility into different subtopics, and it issues a multitude of queries simultaneously connected your behalf.”
AI hunt systems usage query fan-out to heighten their responses. In fact, Google’s charismatic hunt archiving mentions query fan-out arsenic a core technique for retrieving contented from its hunt scale to make AI answers.
How query fan-out works
Query fan-out useful by identifying a prompt’s intents, retrieving contented that fulfills them, and past synthesizing an reply from the astir applicable results.
Broadly, this exemplary comprises 5 components:
- Analysis of the punctual to understand its intents, aliases what the personification is looking for. For example, the punctual “What’s the champion laptop for a assemblage student who needs agelong artillery life and does immoderate video editing?” indicates that the personification is looking for a laptop that’s suitable for assemblage use, has a agelong artillery life, and tin support video editing work. AI frankincense needs to make an reply that fulfills each 3 intents.
- Decomposition, aliases breaking down, of the punctual into aggregate subqueries that reside the identified intents. AI mightiness divided our illustration punctual supra into subqueries for illustration “best laptops for assemblage students,” “laptops pinch agelong artillery life,” and “video editing laptop requirements.”
- Retrieval, wherever the AI strategy searches sources for illustration proprietary and hunt motor indexes for contented that addresses immoderate of the prompt’s subqueries. The strategy whitethorn besides subsequently behaviour much searches based connected what it discovers from first results.
- Scoring of each retrieved contented based connected really good it addresses each subquery, to place and rank the results astir applicable to the prompt.
- Synthesis, wherever the AI strategy uses the highest-scoring contented to make its answer
The implication? Pages that reside the intents down aggregate subqueries are much apt to beryllium referenced and cited by AI. Addressing conscionable 1 wide intent, corresponding to 1 target keyword, is nary longer enough.
Types of fan-out queries
Types of fan-out queries see reformulation, implicit, comparative, recency, contextual variation, and next-step. Here’s what they mean and query fan-out examples for each.
| Fan-out query type | Definition | Original prompt | Fan-out query example |
| Reformulation | Rephrasing of the original punctual to relationship for really users tin definitive the aforesaid intent successful galore ways | “set up a Google Business Profile” | “create a Google Business listing” |
| Implicit | Identifying underlying needs that users don’t expressly mention successful their prompt | “wheelchair-friendly tourer attractions” | “tourist attractions pinch elevator access” |
| Comparative | Assessing 2 aliases much things against each other | “standing table options” | “electric vs manual opinionated desks” |
| Recency | Queries wherever having the latest, most-updated accusation is key | “f1 title dates” | “f1 title dates 2026” |
| Contextual variation | Modification of the punctual to relationship for users’ individual characteristics | “gyms pinch childcare” | “gyms pinch childcare successful [user’s city]” |
| Next-step | Addressing users’ consequent needs aft they get the reply to their first query | “how to registry a trademark” | “trademark registration lawyer” |
One caveat astir these queries: they're synthetic and probabilistic. AI systems make them connected the fly, they displacement from 1 tally to the next, and astir transportation small aliases nary hunt measurement of their own. Treat them arsenic intent signals to cover, not nonstop phrases to target.
All these fan-out query types constituent to circumstantial personification intents, for illustration wanting the latest accusation (recency queries) aliases accusation tailored to their business (contextual variety queries). Being alert of these intents and fulfilling them successful your contented are cardinal for optimizing for query fan-out successful AI.
Why optimizing for query fan-out matters for hunt motor and AI visibility
Optimizing for query fan-out matters for hunt motor and AI visibility because brands whose pages reside users’ intents much thoroughly whitethorn summation higher hunt motor visibility and much AI mentions and citations.
Brands that screen applicable subqueries guidelines to characteristic successful Google's AI Overviews, which whitethorn look astatine the apical of the hunt engine's results pages. Our AI hunt postulation study recovered this visibility progressively shapes wherever find starts. Just for illustration really this Google AI Overview prominently recommends Samsung and Apple's phones successful consequence to the hunt query "best telephone for performance videos":
The AI Overview recommends these brands, citing websites for illustration Digital Camera World successful support, moreover though nary of their pages rank #1 connected the hunt results.
AI hunt platforms whitethorn besides reference and mention a brand’s pages much often erstwhile their contented consistently appears applicable to fan-out queries.
Our acquisition updating 4 blog articles to target applicable fan-out queries points successful this direction, though the effect was directional alternatively than guaranteed. Within a month, these articles' citation counts for our tracked prompts much than doubled, from 2 to astir five. The climb was volatile, spiking arsenic high arsenic nine earlier settling, and marque mentions really dipped complete the aforesaid period. We dainty this arsenic encouraging early evidence, not a settled result.
How to find fan-out queries
To find fan-out queries your contented doesn’t screen yet, usage devices that tally your target prompts successful AI systems to aboveground these prompts’ imaginable subqueries. We stock 2 of these devices below.
Once you person a database of subqueries, onslaught retired those your contented already addresses, truthful you tin attraction connected optimizing for the rest.
AI systems make responses probabilistically. So, the aforesaid punctual tin nutrient different fan-out queries each clip you tally it. We propose identifying trends successful subquery intents alternatively than trying to nail down circumstantial subquery wording.
1. Semrush
Semrush is simply a hunt visibility level that helps brands way and amended their prominence successful hunt motor results and AI answers. The platform’s AI Visibility Toolkit approximates the subqueries that AI systems whitethorn make for your target prompts, while Enterprise AIO shows Google’s existent fan-out queries astatine scale.
To find fan-out queries pinch AI Visibility Toolkit, participate your target taxable into the Prompt Research study and click “Analyze.”
The “Topics” tab of the report’s “Related topics” conception shows topics related to your prompt. Click immoderate taxable to position its applicable sub-prompts, which AI systems whitethorn besides usage arsenic fan-out queries.
To position Google’s existent fan-out queries for prompts, usage Enterprise AIO’s Query Fan-Out Analysis AI Automations.
2. Query Fan-Out Tool
Queryfanout.io is simply a free instrumentality that identifies subqueries by replicating Google's query fan-out process for your target prompt.
Other options for uncovering fan-out queries see ChatGPT Query Fan-Out Tool, a free query fan-out generator for Chrome browsers.
How to optimize for query fan-out
Once you’ve identified applicable fan-out queries, usage these steps to optimize your pages for them:
1. Identify halfway topics
Identify halfway topics to build your AI visibility around, truthful you attraction your query fan-out optimization efforts much effectively.
Start pinch topics straight related to your marque and what you offer. Doing this helps you:
- Control really AI systems represent your brand successful their answers
- Show up during customer travel stages wherever visibility and power matter most
- Leverage your authority since you’re an master successful these topics
Identify your brand’s astir important topics pinch the Questions study successful Semrush’s AI Visibility Toolkit. To do this, participate your domain into the report. Then, position the topics successful the “Topic Distribution” section.
Next, position the report’s “Intent by Topic” conception to study the ascendant intent for each topic. This accusation is adjuvant for mapping the topics to customer travel stages.
After identifying brand-related topics, grow your investigation to screen related topics that align pinch your brand’s expertise. Prioritize these related topics based connected your business goals and assemblage interests.
For example, astatine Semrush, we people contented that covers not conscionable our hunt visibility tools, but besides broader integer trading topics.
2. Plan taxable clusters
Plan the taxable clusters you’ll people contented astir to screen your identified fan-out queries.
Topic clusters are groups of interlinked webpages that collectively screen a halfway taxable successful depth. They're made up of a cardinal pillar page, which provides a wide overview of the halfway topic, and respective cluster pages, which screen applicable subtopics successful aggregate subsections, including ones that reside fan-out queries.
Topic clustering tin promote AI systems to prioritize mentioning and citing your content. It builds topical authority while straight addressing the subqueries these systems whitethorn make during query fan-out.
You tin create a mind representation for illustration this to scheme your taxable clusters:
If you request thief identifying subtopics, usage Semrush's Keyword Strategy Builder. Enter your halfway taxable (like a taxable you recovered pinch the AI Visibility Toolkit), and the instrumentality groups the related queries into taxable clusters, pinch a suggested page for each one.
Where relevant, usage those clusters arsenic your cluster pages' topics, and the queries grouped nether each 1 arsenic subsections wrong those pages.
Then, representation your identified fan-out queries arsenic caller aliases existing cluster page subsections. If your fan-out queries don’t fresh wrong immoderate existing cluster page, commencement caller ones for them.
3. Write NLP-friendly content
Write earthy connection processing (NLP)-friendly contented for your taxable cluster pages truthful that AI systems tin amended understand really their contented fulfills applicable fan-out query intents.
NLP is simply a branch of artificial intelligence that helps computers process, understand, and make matter successful quality language. Writing successful ways for illustration these tin amended really AI systems understand your content:
- Write successful chunks. Chunks are self-contained, meaningful sections of contented that tin guidelines connected their own, letting AI systems process, retrieve, and summarize them much easily. Write successful afloat sentences, and restate discourse wherever helpful.
- Provide definitions. When you present a caller concept, supply a clear and nonstop meaning of it. Doing this helps AI systems understand what you’re talking about, particularly erstwhile they usage a fan-out query for illustration “[topic] definition” to get a definition.
- Structure contented effectively. Add descriptive subheadings to break your contented into sections, and usage heading tags to show their hierarchy. Structuring your contented for illustration this helps AI systems place contented related to highly circumstantial subqueries. You tin besides usage tables and lists to create easy parsable information. Our guideline to optimizing contented for AI hunt engines goes deeper connected each of these.
- Use clear language. Avoid jargon, overly analyzable condemnation structures, and unnecessary fluff. Writing intelligibly makes it easier for AI systems to understand your contented and extract valuable information.
4. Apply schema markup
Apply schema markup to your pages to thief AI systems parse and extract their contented for applicable subqueries.
Schema markup is simply a method of formatting information successful your pages' HTML to awesome to machines, for illustration hunt engines and AI systems, the types of contented these pages contain.
For example, you tin usage Product schema markup to adhd machine-readable labels to a product’s sanction and size:
<script type="application/ld+json">
{
"@context": "https://schema.org/",
"@type": "Product",
"name": "Nike Air Force 1 '07 Men's Shoes",
"size": "US 8"
}
</script>
With these labels, AI systems tin amended place your product’s sanction and size for addressing subqueries astir either attribute.
Check retired our guideline to schema markup to study much astir the disposable schema markup types and really to instrumentality them connected your pages.
5. Build your off-site presence
Build your brand's beingness connected third-party sites, because AI systems hunt the wider web for answers to fan-out queries, not conscionable your site. A larger off-site footprint gives these systems much surfaces to observe your brand's relevance to subqueries, and much chances to mention you successful responses.
Increase your off-site beingness by taking steps for illustration these:
- Set up directory listings: Create a floor plan for your marque connected directories applicable to your industry. Examples of directories are G2 for package businesses and Yelp for restaurants. If you run a section business, declare your Google Business Profile arsenic well, since it’s a cardinal accusation root for AI systems.
- Run digital nationalist relations campaigns: Secure sum of your latest achievements, products, aliases findings, connected reputable manufacture publications. This sum feeds AI systems caller answers to subqueries astir your brand’s credibility and latest news.
- Participate successful organization forums: Participate successful organization forums: Provide adjuvant responses connected Reddit, Quora, and different forums, particularly wherever discussions subordinate to communal subquery intents, to promote AI systems to reference your input successful answers.
6. Optimize for commercialized queries
Optimize for commercialized queries by addressing the fan-out queries AI systems make astir your offerings' characteristics, for illustration their color, model, price, free scheme availability, aliases operating hours, arsenic relevant. Doing this helps AI systems urge your offerings successful consequence to applicable prompts much often.
Tactics for optimizing for commercialized fan-out queries include:
- Address imaginable fan-out queries directly by adding applicable contented to your merchandise and work pages. For example, if you’ve identified “[product name] refund policy” arsenic a imaginable fan-out query, you could adhd a mobility for illustration “What is the refund argumentation for [product name]?” to your merchandise listing’s FAQs section, followed by your reply to it.
- Publish comparison content to style the AI communicative astir really your offerings stack up against competitors’ successful consequence to fan-out queries for illustration “[your product] vs [competing product] review”
- Get much personification reviews, arsenic they thief corroborate accusation astir your offerings, for illustration whether they’re existent to size. Positive reappraisal connection tin besides power AI systems to picture your offerings much favorably. These effects matter astir erstwhile users are actively researching whether your offering meets their needs.
7. Check your subquery coverage
Check your subquery sum to place really good your contented addresses your target fan-out queries’ intents, truthful you tin return steps to amended coverage.
List your pages, and past representation them against the taxable clusters and fan-out queries you’ve identified earlier. Flag fan-out queries that your pages presently don’t address.
Next, cheque the value of contented connected pages that do reside fan-out queries. Your content’s value affects whether AI systems usage your pages to make answers, truthful emblem pages whose contented appears bladed aliases irrelevant to your target subqueries.
Finally, plug your identified gaps. Create caller pages for fan-out queries you deficiency sum for. Also, update existing pages to supply much direct, applicable answers to subqueries that request stronger coverage.
Repeat this sum audit, and hunt for caller fan-out queries, each quarter. Regular attraction will thief you prime up and capable caller contented gaps arsenic your audience’s preferences evolve.
Bonus: Mini lawsuit study
As a bonus, here’s a mini lawsuit study of really Stripe’s trading efforts show galore principles of query fan-out optimization.
The brand’s tract has solutions pages tailored to different business stages, business models, and usage cases. In turn, these pages person subsections that supply direct, elaborate accusation connected applicable subtopics.
This elaborate and varied accusation apt helps AI systems admit Stripe’s relevance to various intents and extract useful accusation for fan-out queries.
The Stripe website besides covers applicable topics done resources for illustration its blog, customer stories, support center, and newsroom.
In the guideline below, Stripe uses clear structuring to break down a analyzable topic. And provides direct, easy-to-understand explanations throughout.
Beyond its website, Stripe often features successful property sum from reputable manufacture publications and news sites, which helps grow its visibility connected the wider web.
Stripe’s AI hunt visibility importantly outperforms competitors’, according to information from Semrush’s AI Visibility Toolkit. Several factors apt explicate this, but the breadth and extent of the brand's on-site content, positive its extended off-site presence, could person played an particularly important role.
Measure your fan-out coverage
Measure your fan-out sum pinch a instrumentality for illustration Semrush’s AI Visibility Toolkit, which reports connected your brand’s visibility and communicative successful AI systems.
Enter your domain into the tool, and its Narrative Drivers study will show your stock of sound for non-branded queries crossed AI hunt platforms. In different words, really often these platforms mention you arsenic opposed to (or alongside) your competitors for queries that don’t incorporate names of brands.
You tin moreover spot if your marque is mentioned first, second, aliases further down successful various AI systems’ responses to circumstantial prompts.
The “Key Sentiment Drivers” conception of the tool’s Perception study provides penetration into your brand’s portrayal successful AI responses, too.
Work to stress strengths and mitigate weaknesses, truthful you make much affirmative sum successful AI responses. And yet pull much customers.
FAQs
Does query fan-out use to ChatGPT and different LLMs, aliases conscionable Google AI Mode?
Query fan-out applies to ChatGPT and galore different LLMs, not conscionable Google AI Mode. Google coined the word to picture its accusation retrieval method for AI Mode, and the manufacture has since adopted "query fan-out" arsenic a wide word for the balanced process successful LLMs for illustration ChatGPT and Claude. None of the companies that create these LLMs person shared their ain sanction for it.
How galore subqueries does AI tally per prompt?
The number of subqueries AI runs per punctual depends connected the prompt’s complexity and really powerful the AI’s exemplary is. An AI strategy mightiness tally conscionable 1 subquery for a elemental prompt, but occurrence disconnected anyplace from 8 to 20+ subqueries for a analyzable one, particularly if you alteration the system’s heavy investigation mode.
Are fan-out queries the aforesaid arsenic long-tail keywords?
No, fan-out queries aren’t the aforesaid arsenic long-tail keywords. While some whitethorn look similar, long-tail keywords are the merchandise of existent quality hunt behavior, being phrases that group person typed into a hunt bar. In contrast, fan-out queries are synthetic. AI systems make them connected the alert to retrieve a larger number of applicable results for building fuller answers.
How do I find the fan-out queries for a topic?
To find fan-out queries for a topic, usage devices that tin tally prompts related to it, and past aboveground their imaginable subqueries. For example, Semrush’s AI Visibility Toolkit approximates the subqueries that AI systems whitethorn make for your target prompts, while Enterprise AIO shows Google’s existent fan-out queries astatine scale.
Does schema markup thief pinch query fan-out?
Schema markup supports query fan-out by adding machine-readable labels to information connected your pages, which helps AI systems parse and extract it accurately. It isn't a citation lever connected its own. Types of information you tin adhd schema markup to see merchandise and work information, and often asked questions and answers.
How does query fan-out impact ecommerce and merchandise pages?
Query fan-out affects ecommerce and merchandise pages by determining the pages AI systems aboveground erstwhile they break commercialized queries into subqueries astir specifics for illustration color, model, and price. These systems are much apt to reference, and make answers using, pages that reside these specifics clearly.
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