What Is Question Fan-Out?
Question fan-out is an AI search system course of that splits a consumer question into a number of sub-queries, collects info for every sub-query, then merges related info right into a single response.
AI search methods (also referred to as LLMs) like Google AI Mode and ChatGPT use question fan-out to enhance the standard of their responses.
Right here’s an illustrative instance of how question fan-out works:
Question Fan-Out in Google AI Mode
Google popularized the time period “question fan-out” when introducing Google AI Mode, a conversational AI interface obtainable inside Google Search.
Within the Google I/O 2025 keynote speech, Head of Search Elizabeth Reid stated: “AI Mode isn’t simply providing you with info—it’s bringing a complete new stage of intelligence to look. What makes this potential is one thing we name our question fan-out method.
“Now, beneath the hood, Search acknowledges when a query wants superior reasoning. It calls on our customized model of Gemini to interrupt the query into totally different subtopics, and it points a large number of queries concurrently in your behalf.”
Whenever you search in Google AI Mode, you may see the mannequin run a number of net searches as a part of its reasoning course of.
On this instance, Google appears to separate the consumer’s question into eight searches:
This question fan-out permits Google’s AI to supply a extremely particular response:
In conventional search outcomes, Google seems to be for the very best direct match to the consumer’s question. However as this instance exhibits, a passable match doesn’t at all times exist.
Why Do LLMs Use Question Fan-Out?
LLMs use question fan-out to higher fulfill search intent (what the consumer needs). Contemplating totally different angles and interpretations of the consumer’s question permits the AI system to supply richer responses that cater to customers’ specific and implicit wishes.
Within the instance beneath, ChatGPT addresses varied sorts of intent to maximise the response’s helpfulness:
Question fan-out additionally permits AI methods to reply advanced, layered queries that have not been clearly answered on-line earlier than. As a result of the system can mix a number of items of knowledge to attract new conclusions.
Right here’s a snippet of a ChatGPT response to a extremely particular question:
Why Does Question Fan-Out Matter in Advertising?
Question fan-out issues in advertising as a result of it permits AI methods to generate extremely particular responses, which can scale back customers’ reliance on different info sources.
This implies AI responses can have an enormous affect on client selections. And guaranteeing your model is featured favorably in related conversations might be key to reaching and interesting your viewers—particularly as AI adoption will increase.
Should you optimize your content material for question fan-out, you could possibly improve your AI visibility via:
- AI mentions: mentions of what you are promoting inside AI responses
- AI citations: linked references to your content material alongside AI responses
Right here’s an instance of an AI point out and an AI quotation in ChatGPT:
Question fan-out requires a specialist method as a result of it really works in a different way than conventional search algorithms. That stated, optimizing for question fan-out can increase your efficiency in conventional search, too.
Methods to Optimize for Question Fan-Out
To optimize for question fan-out, it is best to determine core matters, cowl these matters comprehensively, write for pure language processing (NLP) algorithms, and use schema markup.
That is along with following different LLM optimization finest practices.
1. Establish Core Matters
First, determine core matters to construct your AI visibility round. This can make it easier to to focus your optimization efforts extra successfully.
I like to recommend that you simply begin with matters straight associated to what you are promoting and what you supply. This helps you:
- Management how your model is portrayed in AI-generated responses
- Present up throughout key phases of the purchaser’s journey, the place visibility and affect matter most
- Leverage your authority, since these are areas the place you are clearly the knowledgeable
You’ll be able to determine a very powerful model matters via Semrush’s AI Toolkit. For instance, you may discover that persons are extra fascinated by social accountability than expertise and innovation.
When you’ve recognized brand-related matters, increase into associated areas aligned together with your model’s experience. Ensuring to prioritize based mostly on what you are promoting objectives and viewers pursuits.
For instance, at Semrush, we publish content material about our digital advertising instruments and broader digital advertising matters.
2. Plan Subject Clusters
Subject clusters are teams of interlinked webpages that work collectively to cowl a core matter comprehensively. They’re made up of a central pillar web page, which gives a broad overview of the core matter, and several other cluster pages, which cowl related subtopics.
Subject clustering lets you handle a number of queries that could be generated via related question fan-outs, that means you could have a better probability of that includes in AI responses.
It additionally lets you construct topical authority, which may encourage AI methods to prioritize your solutions over others.
You’ll be able to create thoughts maps to plan your matter clusters. Like this:
Should you need assistance figuring out subtopics, use Semrush’s Subject Analysis device. All you should do is enter your core matter alongside together with your goal nation.
The device will present a listing of subtopics with particular questions for every. These questions will make it easier to to create complete content material, as described within the subsequent step.
3. Create Useful, Complete Content material
Creating useful, complete content material is vital to answering the various sub-queries that may consequence from question fan-out.
Break down every subtopic into much more particular questions. Then handle these intents via subsections of your web page.
Right here’s an illustrative instance of a core matter splitting into subtopics and people subtopics splitting into particular queries:
You’ll be able to determine particular intents to cowl by:
- Performing key phrase analysis—e.g., utilizing a device to see what queries folks sort into Google
- opponents’ content material—e.g., seeing what rivals cowl of their FAQs
- Exploring related on-line communities—e.g., seeing what questions customers ask in related boards
- Consulting your group—e.g., asking your customer support group what questions come up most
Should you use Semrush’s AI Toolkit, you possibly can uncover particular brand-related questions that folks ask in LLMs. Addressing these queries in your content material could make it easier to affect clients at key phases of the shopping for journey.
4. Write for NLP
AI methods use pure language processing (NLP) to grasp written content material, so writing for NLP will help you seem in AI responses.
Listed below are some recommendations on writing for NLP:
- Write in chunks. Chunks are self-contained, significant sections of content material that may stand on their very own and be simply processed, retrieved, and summarized by an AI system. Write in full sentences and restate context the place useful.
- Present definitions. Whenever you introduce a brand new idea, present a transparent and direct definition. This can assist AI methods perceive what you’re speaking about, and so they could hunt down definitions as a part of the question fan-out course of.
- Construction content material successfully. Add descriptive subheadings to interrupt your content material into sections and use heading tags to indicate their hierarchy. This can assist AI methods determine content material associated to extremely particular queries. You may also use tables and lists to create simply parsable info.
- Use clear language. Use clear, conversational language. Keep away from jargon, overly advanced sentence constructions, and pointless fluff. This can make it simpler for AI methods to grasp your content material and extract worthwhile info.
5. Use Schema Markup
Schema markup permits you to add machine-readable labels to several types of information on a web page, and these labels might assist AI methods interpret your content material extra precisely.
For instance, you need to use Product schema to label a product’s title and picture. And use Provide schema to label the product’s value and availability.
Like this:
This schema could make it simpler for AI methods to extract related info it makes use of for answering product-related queries. Like so:
Head to Schema.org to determine schema sorts that is likely to be related to your web site. You may also discover recommendation on tips on how to implement structured information.
Bonus: Mini Case Research
The Stripe web site demonstrates many ideas of question fan-out optimization.
For instance, the web site has options pages tailor-made to totally different enterprise phases, enterprise fashions, and use instances. These pages have subsections that present direct, detailed info on related subtopics.
This detailed and diversified info doubtless helps AI methods acknowledge Stripe’s relevance to varied intents and extract helpful info for fanned-out queries.
The Stripe web site additionally covers related matters via its weblog, buyer tales, help heart, newsroom, and different sources.
Within the information beneath, Stripe makes use of clear structuring to interrupt down a fancy matter. And gives clear, direct explanations all through.
Stripe considerably outperforms its opponents by way of AI search visibility, in response to information from Semrush’s AI Toolkit. This is because of quite a lot of elements, however the breadth and depth of high quality on-site content material might play an necessary function.
Begin Measuring Your Efficiency in AI Search
Measure the success of your question fan-out optimization technique with Semrush’s AI Toolkit.
The toolkit exhibits your share of voice for a number of non-branded queries throughout a number of AI platforms. This exhibits how typically LLMs point out you versus (or alongside) your opponents.
You’ll be able to even see in case your model is talked about first, second, or additional down in response to particular prompts.
The device gives perception into your model’s portrayal in AI responses, too.
Working to emphasise what you are promoting’s strengths and mitigate its weaknesses permits you to generate extra optimistic protection in AI responses. And in the end entice extra clients.
