What Is Query Fan-Out?
Query fan-out expands a user's question into multiple related searches, retrieves relevant information, and combines it into an answer. Google documents this technique for AI Mode and AI Overviews; other search-enabled AI products can also rewrite prompts into targeted searches. Understanding it helps teams investigate how their brands and sources appear in AI Search.
An AI search product combines a language model with retrieval and other tools. It is not the same thing as a standalone LLM. Query fan-out describes a retrieval technique, not every model's internal reasoning, and not every AI response searches the web. The queries, sources and number of retrieval steps depend on the product and question.
Live retrieval supplies information for an answer at response time. It does not mean that the model immediately learns a webpage into its internal parameters. Updating a page can improve the evidence available to search without guaranteeing when an AI product will retrieve or cite it.
In this article, we’ll break down how query fan-out works, why it matters for brands, how it affects AI search visibility, and the practical steps you can take to optimize your content for it.
Here’s an illustrative example of how query fan-out works:
Query fan-out is not just theory, it’s already in action inside modern AI search engines.
Query Fan-Out in Google AI Mode
Google described query fan-out when introducing AI Mode in March 2025, before explaining it again at Google I/O in May 2025.
At Google I/O 2025, Head of Search Elizabeth Reid explained that AI Mode breaks a question into subtopics and issues multiple queries simultaneously. This lets Search retrieve information beyond the original wording of a question.
Google's current site-owner guidance describes fan-out as concurrent related searches. It also confirms that existing SEO best practices remain relevant and that no special schema is required for generative AI features in Search.
Read Google's explanation from Google I/O 2025.
When you search in Google AI Mode, you might see the model run multiple web searches as part of its reasoning process.
In this example, Google seems to split the user’s query into eleven searches:
This query fan-out enables Google’s AI to provide a highly specific response:
Similar multi-search behavior is documented in other products. OpenAI says ChatGPT Search can rewrite a prompt into one or more targeted queries and send follow-up searches after reviewing results. Perplexity describes multiple searches in Pro Search. These disclosures do not establish that every Gemini, ChatGPT, Perplexity or Mistral response uses the same retrieval process.
- In a fan-out workflow, the system generates related queries from the user's question and the context available to it. Google has not established that these queries come from the People Also Ask feature.
- Search services retrieve candidate results for those queries. The model can then use relevant information to develop its answer; this is not the same as crawling the entire web or reading everything it knows.
- Retrieval does not mean every returned site is opened, used or cited. A visible answer shows the final output, but does not by itself reveal the complete set of underlying search queries.
- The resulting answer may address several parts of the user's question and link to supporting sources. Broader retrieval can add useful context, but does not guarantee accuracy; readers should check that sources support the answer.
How Query Fan-Out Changes Content Strategy and SEO
Query fan-out gives teams a reason to consider related customer questions alongside individual keywords. It does not establish a deadline after which traditional SEO stops working. Google's July 2026 guidance confirms that foundational SEO remains relevant to generative Search.
So, how do we shift our content strategies in this new query fan-out world where the goal, ultimately, is to be cited as often as possible by LLMs so that your brand is familiar to a prospect and they’re more likely to click an ad or an organic result? This is where Generative Engine Optimization (GEO) principles come into play; aligning topic coverage, semantic structure, and authority so AI systems can reliably identify and cite your content.
There are a few things to take into account.
1. Cover the customer questions that matter to your topic. Create new pages only when they provide distinct value; adding a page for every possible fan-out query is not a requirement.
2. Use structured data where it accurately describes visible content and supports relevant search features. Special AI schema is not required. Google stopped displaying FAQ rich results in May 2026.
3. Use one comprehensive page or several linked pages according to the subject and reader's needs. A larger content corpus does not by itself improve relevance or guarantee citations.
4. If you publish comparisons of services, use explicit evaluation criteria, evidence and clear disclosure of your commercial interest. Ranking yourself first without supporting evidence is not a reliable AI visibility strategy.
Some AI experiences can personalize results using available context. Google described planned AI Mode personalization at I/O 2025, while OpenAI documents how enabled memories can affect search-query rewriting. Personalization and available data vary by product; they do not establish one universal retrieval process.
Ultimately, Google’s business works only when they can serve you the best result for your query (to keep you happy so you come back time and time again). So if you’re producing content, you need to provide the best answer so they can take it and use it in AI, hopefully citing you along the way.
Why Do LLMs Use Query Fan-Out?
Search-enabled AI systems can use query fan-out to investigate different parts of a question and retrieve relevant evidence. A standalone model answering without search does not perform this web-retrieval step.
Related searches can surface different perspectives, comparisons and supporting facts. This can improve coverage of a complex question, but the usefulness of the answer still depends on source quality and how the information is interpreted.
The ChatGPT example below illustrates a structured answer to a question with several possible considerations. The visible answer alone does not demonstrate that multiple searches occurred.
A search-enabled system can combine information from multiple sources to address a question that no single page answers in full. That synthesis can be useful, but any new conclusion still needs to be checked against the underlying evidence.
Here is a ChatGPT response to a question with several constraints. It illustrates the specificity of the output, rather than proving an unseen query fan-out process.
Why Does Query Fan-Out Matter in Marketing?
Query fan-out matters in marketing because it enables AI systems to generate highly specific responses, which may reduce users’ reliance on other information sources.
AI responses can influence which products and brands users consider. Appearing accurately in relevant answers is therefore worth measuring as adoption grows.
If you optimize your content for query fan-out, you may be able to increase your AI visibility through:
- AI mentions: mentions of your business within AI responses
- AI citations: linked references to your content alongside AI responses
Here’s an example of an AI mention, an AI citation and direct product purchase link in ChatGPT:
And in this image you can see the Brand being mentioned with a direct product link provided as well.
Clear, useful and crawlable content can support both traditional search and AI Search visibility. Neither a topic cluster nor query fan-out optimization guarantees higher rankings or citations.
ChatGPT Query Fan-Out Research: Historical Findings and Current Limits
Nectiv's October 2025 study analyzed more than 8,500 commercially oriented prompts across nine verticals using its AI tracker. The following results describe that sample and period, not a universal or current ChatGPT search rate.
- In Nectiv's October 2025 sample, approximately 31% of prompts recorded web-search activity.
- Local-intent prompts recorded search activity in 59% of instances in the same sample.
- Commerce prompts recorded search activity in 41% of instances, compared with 18% for Credit Cards and 19% for Fashion.
- Nectiv reported an average of 2.17 search queries and 5.48 words per search query. Its report uses several overlapping descriptions of the search-count denominator, so these averages should not be applied to every ChatGPT prompt or treated as a fixed product limit.
- Jobs & Careers recorded nearly three searches on average in Nectiv's analysis, while Software averaged 2.68 queries.
- Nectiv identified recurring terms in extracted search queries, including reviews, 2025, features and comparison, with free especially prominent in Software. These are observed query terms, not proof that inserting a word causes ChatGPT to search.
For current adoption figures and a comparison of search-rate studies with different methodologies, see our ChatGPT Statistics guide.
How to Optimize for Query Fan-Out
To prepare content for related retrieval queries, identify important customer questions, provide accurate answers and supporting evidence, and make pages crawlable and easy to navigate. Treat topic planning as a way to serve readers, not a requirement to create content for every possible query variant.
This is in addition to following other content optimization for Generative AI best practices.
1. Identify Core Topics
First, identify core topics to build your AI visibility around. This will help you to focus your optimization efforts more effectively.
I recommend that you start with topics directly related to your business and what you offer. This helps you:
- Give AI systems accurate information about your brand; you cannot directly control their final answers.
- Show up during key stages of the buyer’s journey, where visibility and influence matter most
- Leverage your authority, since these are areas where you're clearly the expert
You can identify the most important brand topics through Superlines’ data. For example, you might find that people are more interested in social responsibility than technology and innovation.
Once you’ve identified brand-related topics, expand into related areas aligned with your brand’s expertise. Making sure to prioritize based on your business goals and audience interests.
For example, at Superlines, we publish content about our AI Search solution and broader digital marketing topics.
2. Plan Topic Clusters
Topic clusters are groups of interlinked webpages that work together to cover a core topic comprehensively. They’re made up of a central pillar page, which provides a broad overview of the core topic, and several cluster pages, which cover relevant subtopics.
Topic clustering helps you to address multiple queries that may be generated through relevant query fan-outs, meaning you may have a greater chance of featuring in AI responses.
Internal links help readers and search systems discover relevant supporting pages. Topic clusters can organize that information, but do not guarantee priority over other sources.
3. Create Helpful, Comprehensive Content
Creating helpful, comprehensive content is key to answering the diverse sub-queries that can result from query fan-out.
Break down each subtopic into even more specific questions. Then address these intents through subsections of your page.
4. Write for NLP
Clear writing helps readers and AI systems interpret content. It does not require a separate writing style designed solely for NLP algorithms.
Here are some tips on writing for NLP:
- Use sections that make sense to readers. State the subject clearly and give enough context for key facts to stand on their own. There is no mandatory chunk size or ideal page length for Google's generative AI features.
- Provide definitions. When you introduce a new concept, provide a clear and direct definition. This will help AI systems understand what you’re talking about, and they may seek out definitions as part of the query fan-out process.
- Structure content effectively. Add descriptive subheadings to break your content into sections and use heading tags to show their hierarchy. This will help AI systems identify content related to highly specific queries. You can also use tables and lists to create easily parsable information.
- Use clear language. Use clear, conversational language. Avoid jargon, overly complex sentence structures, and unnecessary fluff. This will make it easier for AI systems to understand your content and extract valuable information.
5. Use Schema Markup
Structured data labels information such as products, prices and availability in a machine-readable format. Keep it consistent with visible content. Google does not require special schema for generative AI features, and markup does not guarantee inclusion.
For example, you can use Product schema to label a product’s name and image. And use Offer schema to label the product’s price and availability.
Start Measuring Your Performance in AI Search
Measure the success of your query fan-out optimization strategy with Superlines.
The toolkit shows your share of voice for a selection of non-branded queries across multiple AI platforms. This shows how often LLMs mention you as opposed to (or alongside) your competitors.
You can even see if your brand is mentioned first, second, or further down in response to specific prompts. The tool provides insight into your brand’s portrayal in AI responses, too.
Use visibility and citation data to identify inaccurate descriptions, missing evidence and useful content improvements, then measure whether those changes help. Positive coverage and additional customers are possible outcomes, not guaranteed results.
Superlines is an AI Search analytics platform for enterprise marketing teams and agencies. It helps teams compare brand visibility, citations, sources, positioning and share of voice across AI engines and markets. Start with a set of relevant customer questions, measure your current visibility, and test improvements against that baseline.