_____ Query fan-outs guide

AI systems such as ChatGPT, Gemini and Perplexity don’t always answer a prompt by running a single search. Instead, they often expand the original question into multiple related searches to research the topic before generating a response.

During this process, the model may explore different aspects of the query, retrieving information from a range of sources. These additional searches are known as ‘query fan-outs’.

Understanding how query fan-outs work is becoming increasingly important for generative engine optimisation (GEO), because they reveal how AI systems actually research and create answers.

They influence:

• Which sources AI systems retrieve
• Which brands appear in AI-generated answers
• How content is discovered during AI research

As a specialist GEO agency, we analyse this behaviour through controlled experiments to understand how AI models retrieve information, expand prompts and determine which sources to cite.

Let’s break down what query fan-outs are, how they work, and how AI models expand queries when researching a topic.

What are query fan-outs?

A query fan-out describes how an AI model expands a single user prompt into multiple related questions or searches when researching a topic.

Rather than relying on one query, the model generates several additional searches that explore different aspects of the topic. These searches allow the system to find relevant information across websites before producing an answer.

The results are then combined to generate a single AI response.

In simple terms, the process looks like this:

This is the process AI systems use to research a topic before producing a response.

Why query fan-outs matter for AI search

Query fan-outs influence which sources AI systems discover when researching a topic.

When an AI model expands a prompt into multiple related searches, each query can retrieve different sources and perspectives. The model then combines these results to produce its final answer.

This means visibility in AI-generated responses is not determined by ranking for a single keyword. Content can be discovered through many related searches during the model’s research process.

For brands and publishers, this changes how content appears in AI systems. Pages that cover a topic in depth and answer several related questions are more likely to be retrieved across these fan-out searches, strengthening topical authority.

What query fan-outs reveal about visibility

By analysing query fan-outs, you can see how AI systems actually research a topic.

Each fan-out query can retrieve different sources and perspectives, revealing how the model builds its understanding of a topic. Mapping these prompts reveals how the model researches a topic and which sources it retrieves.

This analysis can reveal:

 

Which sources are consistently retrieved

 

 

Which topics models explore around a query

 

 

Where competitors appear across these searches

 

 

Where your content is missing entirely

 

These insights help explain why some brands appear regularly in AI-generated answers while others remain invisible.

What does a query fan-out look like?

To understand query fan-outs, it helps to think about how an AI system researches a question.

For example, a user might ask: "What are the best family holidays?"

From here, the model will expand the prompt into several related queries, such as:

 

Best destinations for family holidays

 

 

Family holiday resorts with kids clubs

 

 

Cheapest family holidays in Europe

 

 

Best family beach holidays

 

 

Family holidays with waterparks

 

Each of these searches explores a different aspect of the topic and helps the model find relevant information before generating its final answer.

Why AI models use query fan-outs

Large language models (LLMs) aim to answer a user’s question as accurately and completely as possible.

To do this, they expand the original prompt into several related questions that help them build a fuller understanding of the topic at hand. These additional searches help the model gather information from different sources before generating its response.

Query fan-outs help AI models:

 

Understand the intent behind a question

 

 

Find information from multiple sources

 

 

Check facts across different websites

 

 

Build more comprehensive answers

 

This is why a single AI response often includes information pulled from many different pages.

How AI models expand queries into fan-outs

When a prompt is submitted to an AI tool, like ChatGPT or Gemini, the model often performs a short research step before generating its answer. During this stage, the model runs several related searches and combines the information it finds into a single response.

This process typically involves three stages:

1. Intent interpretation

The system first determines what the user is actually looking for.

For example, a prompt like "best family holidays" may imply:

• Destination ideas
• Travel tips
• Price comparisons
• Resort recommendations

2. Query expansion

The model then generates multiple related searches that explore the topic more comprehensively.

These queries often include:

• Synonyms
• Related topics
• Follow-up questions
• Comparisons

3. Retrieval and synthesis

Information is retrieved for each expanded query and combined to produce a single response. This is why AI answers often draw on information from several different pages.

The six types of query fan-outs

When analysing how AI systems expand prompts, fan-outs tend to fall into several common patterns. While the exact structure varies between models, most fan-outs include combinations of the following six query types.

These categories help explain how AI systems explore a topic during the research process.

1. Definition queries

Definition queries help the system understand the topic itself and establish the core concepts behind a prompt.

Examples:

• What is a family holiday
• What counts as a family resort
• What makes a destination family-friendly

2. Comparison queries

Comparison queries evaluate different options or approaches in order to generate recommendations.

Examples:

• Best family holiday destinations in Europe
• All-inclusive vs self-catering family holidays
• Cheap family holidays vs luxury resorts

3. Feature queries

Feature queries explore specific attributes that users may care about when making a decision.

Examples:

• Family holidays with waterparks
• Resorts with kids clubs
• Family holidays with short flights

4. Informational queries

Informational queries expand the research around planning, logistics, and travel advice.

Examples:

• Best time to travel with kids
• How to plan a family holiday
• Travel tips for families

5. Transactional queries

Transactional queries relate to booking intent and commercial options.

Examples:

• Cheap family holiday deals
• Last-minute family holidays
• Best family holiday packages

6. Validation queries

Validation queries help models cross-check information across multiple websites before generating its answer.

Examples:

• Reviews of family holiday resorts
• Best travel sites for family holidays
• Forums discussing family travel

Example of a query fan-out

Let’s look at the prompt "best family holidays".

A simplified fan-out might look like this:

Each query explores a different aspect of the topic, helping the model gather the information needed to produce the final answer.

How to analyse query fan-outs

Understanding how query fan-outs work is useful, but analysing them provides much deeper insight into how AI systems research a topic.

A simple process can help you map fan-outs for any search query.

• Step 1: Start with a core prompt
• Step 2: Generate related queries
• Step 3: Review which sources appear
• Step 4: Identify visibility opportunities

For example, mapping the fan-outs for a query like "best family holidays in Europe" often reveals dozens of related prompts, each retrieving different sources and perspectives.

Want to analyse fan-outs for your own industry? Download our query fan-out analysis worksheet.

How query fan-outs influence AI answers

Query fan-outs directly influence which sources appear in AI-generated answers.

When an AI model expands a prompt into multiple related queries, each search retrieves information from different pages. These results are then combined to produce the final response.

Pages that answer several of those related queries are more likely to appear in the model’s research process.

For example, a page covering family holiday destinations, resorts with kids' clubs, and the best time to travel with children may surface in response to several fan-out queries. This increases the likelihood that the page will be retrieved and cited by AI systems.

This is one reason why topical depth and structured content are becoming increasingly important for both SEO and GEO.

Rather than targeting a single keyword, strong pages answer multiple related questions and cover a topic in depth. This makes them more useful for both traditional search engines and AI systems in researching a topic.

These insights often inform broader strategies such as AiPR™ campaigns, which focus on earning authoritative mentions in sources AI systems trust.

How query fan-outs relate to keyword research

Query fan-outs behave very similarly to long-tail keyword research (and targeting ‘low-hanging fruit’), but operate around conversational prompts rather than traditional keywords.

AI models explore networks of related queries when researching a topic. This means search strategies should focus on topic coverage rather than isolated keywords.

Strong pages often cover:

 

The main query

 

 

Related questions

 

 

Comparisons

 

 

Supporting information

 

This is one reason why data-led assets often perform well across both search engines and AI systems. Their depth, originality and topical relevance make them useful across multiple fan-out queries.

Authority signals also influence which sources AI systems retrieve. Editorial coverage and high-quality links earned through Digital PR help strengthen the credibility of pages AI models cite.

Why query fan-outs matter for GEO

Query fan-outs are a core concept within generative engine optimisation (GEO).

When we run a GEO audit, we analyse how AI systems expand prompts into related queries and which websites appear across those searches.

This helps us understand:

• How AI models research a topic
• Which competitors are cited most frequently
• Where visibility gaps exist

For example, analysing travel prompts such as "family holidays" often reveals fan-out queries related to destination comparisons, resort features and travel planning advice.

If a brand only targets the main keyword, it may miss visibility across this wider set of related searches.

Understanding these fan-outs helps brands improve their visibility across multiple AI research paths rather than relying on a single query.

Want to understand how AI systems research your industry?

Our GEO audits analyse prompt fan-outs, citation patterns and brand visibility across major AI platforms to identify where your brand appears in AI answers, and where opportunities exist.

Recap

Query fan-outs are a core part of how AI systems research information.

Understanding them helps explain:

• How AI answers are generated
• Why some sources get cited more often
• How brands can improve visibility in AI search

This means the focus is moving away from targeting keywords to covering topics in depth. The more related questions your content answers, the more opportunities AI systems have to discover, retrieve and cite your pages.

This is how brands 'win' visibility across both traditional search and AI search.

Frequently asked questions about query fan-outs

What are query fan-outs in AI search?

Query fan-outs are the additional searches an AI model generates when researching a topic. Rather than relying on a single query, the system expands the original prompt into several related searches to gather information before producing an answer.

Why do AI models use query fan-outs?

AI models use query fan-outs to better understand the intent behind a question. By exploring several related searches, they can gather information from different sources and produce more complete answers.

How do query fan-outs affect AI visibility?

Query fan-outs influence which pages AI systems retrieve when researching a topic. Content that answers several related queries is more likely to be discovered and cited in AI-generated responses.

How can marketers use query fan-outs?

Marketers can analyse fan-outs to see how AI systems explore a topic. This helps identify content gaps, improve topic coverage and increase the chances of being cited in AI answers.