AI Query Fanouts: What they are and how to optimise for them

Contents 

• What is a Query Fanout? 
• Why Do LLMs Use Query Fanout? 
• How to See Query Fanout in 2026  
• How to Optimise for Query Fanout
• Conclusion: Optimising for AI Query Fanout 
• FAQ: AI Query Fanout

What is a Query Fanout? 

Query fanouts are the sub-questions and searches generated by LLMs when a user asks the engine a question. These sub-queries end up forming the final answer the user sees. 

They’re intended to cover the full breadth of the user’s question, improving the LLM’s final response by pulling information from multiple angles and sources. 

For example, when a user searches for “What are the best hats to wear in Berlin Summer?”, the LLM may produce and research sub-questions such as: 

• “What is the average sun exposure in Berlin during summer?” 
• “What are the most fashionable unisex hats in Europe in 2026?”
   

The research conducted under these “extra questions” then serves to address the original query posed by the user. 

In short: think of query fanouts as the underlying thought process of the LLM as it tries its best to answer your question. 

Why Does Query Fanout Matter in Marketing? 

Query fanout changes how brands get discovered. LLMs don’t list ten blue links – they synthesise a single, specific answer that can directly influence consumer decisions. If your brand isn’t represented in the sub-queries feeding that response, you’re invisible at the point of decision. 

This creates two key opportunities: 

• AI mentions – your brand is named within an AI response, building awareness without the user clicking through to your site 
• AI citations – your content is linked as a source, driving referral traffic and signalling authority
   

The more fanout sub-queries your content answers, the higher the chance of earning both. Brands that optimise for fanout now are building a competitive advantage that compounds over time.

With research suggesting that most prompts trigger between 5 and 11 fan-out queries on average, this provides ample opportunity to get your brand cited.   

Why do LLMs Use Query Fanout? 

At their core, LLMs use query fanout to deliver better, more accurate, and more comprehensive answers. Here’s why this technique has become standard across all major AI search platforms: 

• Improving factual accuracy – by cross-referencing responses against multiple third-party sources, fanout helps reduce hallucinations and grounds the final answer in a wider base of information 
• Handling ambiguous queries – rather than guessing what you mean, the LLM explores multiple interpretations simultaneously (e.g., “red phone case” triggers sub-queries for iPhone, Samsung, and Pixel models at once) 
• Anticipating follow-up questions – AI platforms proactively gather information you’re likely to need next, saving several rounds of follow-up searches (e.g., “symptoms of flu” also triggers sub-queries about diagnosis and treatment) 
• Synthesising complex topics – some questions can’t be answered from a single source, so fanout pulls from web indexes, knowledge graphs, product databases, and more to build one cohesive response 
• Personalising results – sub-queries can be tailored based on user context, location, search history, and behaviour, meaning two people asking the same question might receive quite different answers 

How to see AI Query Fanout in 2026 

LLMs have increasingly hidden the query fanout process from users in 2026. However, by using the Developer tab in your browser, you will be able to see what sub-queries are occurring. This will enable you to optimise your content for these specific searches.

For this example, we’ve used ChatGPT in Chrome, but it should work for whatever LLM and browser you decide to use.

Step 1: Access Developer Tools  

• First, Open ChatGPT (or your LLM of choice) in the browser window. 
• Input your prompt into the chat (e.g., “What are the best hats to wear for Berlin Summer 2026?”) 
• If you’re in chrome, open developer tools by: 
•  Opening customize and control window (the three dots in the upper-right corner) 
• Hover over more tools in the dropdown list  
• Select developer tools 

Step 2: Select the Network Tab & Search for your Chat-ID  

• Select “Network” at the top of the Developer window  
• Select the “Fetch/XHR filter” 
• Copy the last part of the URL (this is the Chat-ID) 
• Paste it into the Developer Window filter field  
• Refresh the page 

Step 3: Select Your Specific Network Request  

• On the left you’ll see a number of network requests. Click on request with the matching chat-ID title. 
• Switch over to the “Response” tab 

Step 4: Find Fanout Queries  

• Search for the term “search_model_queries” 
• This will now highlight the sub-queries the LLM conducted in response to the main prompt.  

Sounds complicated? Check our resources on AI visibility or see how our award-winning teams can help you by enquiring about our LLM Consulting Services. 

How to optimise for query fanout? 

Once you’ve completed the steps outlined in the section above, you’ll be able to identify what sub-questions are being generated by user queries. The critical step is not only gearing your content toward the initial queries, but also the sub-questions that are generated alongside them. 

Here’s a practical framework for optimising your content:

1. Map your topic’s fanout themes and patterns

Start by identifying the recurring patterns in the sub-queries generated for your target topics. Services such as Peec.AI or manual inspection via browser developer tools can help you uncover these. 

Rather than treating each sub-query as a standalone keyword, look for the themes that emerge. Common fanout patterns include: 

2. Build topic clusters around fanout patterns

Use your fanout themes to plan your content architecture. A topic cluster – a pillar page covering the broad topic, supported by cluster pages on specific sub-topics, all connected through internal links – maps naturally to the way LLMs generate sub-queries. 

Expert Tip: This approach also builds topical authority, a signal that encourages AI systems to treat your content as a go-to source 

3. Audit your existing content coverage

Once you’ve mapped the key fanout patterns, audit your content against them. Ask yourself: 

• Which angles do you already cover well? 
• Where are the gaps? Are there sub-topics or dimensions you haven’t addressed at all? 
• Does your content match the intent behind the fanout queries – or does it only scratch the surface? 

Gaps at the site level point to opportunities for new content. Gaps within individual pages suggest areas where existing content can be expanded with additional sections, data, or detail. 

4. Close the gaps – on and off your site

This is where the real optimisation happens. Based on your audit, take action: 

• Expand existing pages – add missing sub-topics (e.g., equipment and hosting in a “how to start a podcast” guide), because the LLM’s fanout queries almost certainly cover them 
• Create new content – where you have no coverage at all, treat fanout patterns as a roadmap for your content strategy 
• Structure content clearly – use descriptive headings, bullet lists, and bold key terms so LLMs can parse and extract answers easily 
• Add structured data – Schema markup for products, FAQs, and how-to content helps AI systems understand your content’s attributes more reliably 
• Build off-site presence – ensure your brand appears on third-party sources that LLMs frequently cite, such as review sites, comparison pages, and industry publications 

 5. Keep content fresh and comprehensive

Fanout queries often include recency modifiers – the LLM actively searches for the latest information. Regularly updating your content with current data, dates, and examples signals to AI systems that your page is a reliable, up-to-date source. 

Key takeaway: Pages that address several dimensions of a topic – rather than just one narrow angle – have better odds of being cited in AI-generated answers. Aim for comprehensive coverage within each piece. Research suggests that content appearing across multiple fanout sub-queries scores higher in the AI’s synthesis process. 

Conclusion: Optimising for AI Query Fanout  

In this article we’ve explored what AI query fanouts are, why they occur, why they’re important, and how to optimise for them.

Here are some of the key takeaways from what we’ve covered: 

• Query fanout is the default – every major AI search platform uses it to build comprehensive answers 
• Visibility depends on breadth – content that covers multiple dimensions of a topic has better odds of being cited 
• Think patterns, not keywords – fanout sub-queries are synthetic and inconsistent, so optimise for recurring themes rather than specific terms 
• Topic clusters map to fanout naturally – a well-structured cluster already mirrors the way LLMs break down a question 
• Freshness matters – LLMs actively search for up-to-date information, so keep your content current 

Want to make sure your content is optimised for AI search? Peak Ace’s SEO team helps brands navigate the evolving search landscape – from traditional rankings to LLM visibility. Get in touch to find out how. 

FAQ: AI Query Fanout 

How many fanout queries does an LLM typically generate? 

Most prompts trigger between 5 and 11 sub-queries, though more complex or ambiguous questions can generate significantly more. ChatGPT Deep Research has been observed running hundreds of searches for a single prompt. 

Do fanout queries have search volume? 

The vast majority don’t. Over 95% of fanout queries receive no recurring search volume – they’re synthetically generated by the AI and often include context-rich modifiers that humans wouldn’t typically type. 

Can I optimise for specific fanout sub-queries? 

It’s better to optimise for the patterns and themes rather than individual sub-queries. Because fanout queries are probabilistic and inconsistent – even the same prompt can trigger different sub-queries each time – focusing on comprehensive topic coverage is more effective than chasing specific terms. 

Does query fanout affect traditional SEO? 

Query fanout optimisation doesn’t usually produce immediate changes in traditional rankings. However, it can increase your content’s inclusion in AI-generated answers, which is becoming an increasingly important source of visibility and traffic.