The Query Fan-Out Model: How AI Chooses What To Cite

Traditional search engine optimization (SEO) aligns content with a core theme or topic and supports it with a cluster of related topics mapped to different search intents. AI search builds on that model but takes it further.

Now, when someone runs a Google search, Google’s AI Overviews and AI Mode break that search into subtopics and issue a query for each one at the same time. Google calls this query fan-out, and it decides which pages are eligible to appear in the answer at all.

Each subtopic in that expansion is a separate chance to be cited. A page with a section for several relevant subtopics can be cited multiple times in the same AI-generated answer.

What Is the Query Fan-Out Model?

Query fan-out describes how AI systems expand a single user query into multiple related sub-queries before assembling an answer from whatever addresses each one. Google defines a fan-out as “a set of concurrent, related queries generated by the model.”

Fan-out behavior differs by AI system. Google’s AI Overviews, AI Mode, and Perplexity tend to retrieve, meaning they search and pull from live pages, even for straightforward definitional queries. ChatGPT and Claude more often answer that same kind of query from training data instead, and retrieve mainly when the question needs something current or specific. Ahrefs reports that AI Mode typically runs five to 11 searches per prompt, and that its own tracking recorded ChatGPT’s Deep Research mode running 420 searches to fully answer one complex shopping query. The count varies enough by platform and query that it matters less than whether your content answers the sub-queries that show up.

How Query Fan-Out Works

If you search “How does schema markup work?” in an AI tool, it breaks the original search into multiple queries like:

• What is schema markup?
• Why is schema important for SEO?
• What types of schema are available?
• How do I implement schema on my website?
• What are common schema mistakes?

The AI search tool retrieves content chunks that address each question, sometimes from one source, often from several, then weaves them into a single response. Each of those questions needs a chunk that can stand on its own as the answer. The questions are sibling subtopics of the original search, all asked at once, so the system reads every result together before it writes anything.

The fan-out process rewards organization, because a system has to isolate the section that answers each sub-question.

Working out which subtopics a query branches into is a research job, and the practical way through it is to walk the question journey your reader takes, from what they ask first to what they compare before deciding. The model generates most of those same questions simultaneously, which is why the journey works as a map even though the machine never travels it.

Common Fan-Out Question Types by Buyer-Journey Stage

Fan-out questions cluster around what the reader is trying to resolve, and each type tends to show up at a different point in how someone decides to work with you:

Sub-Query Type	What It Resolves	Buyer-Journey Stage	Example
Definition/explanation	What something is or how it works	Problem Identification	“Schema markup” branches into what it is and why it matters for SEO
Problem-identification	Whether this is the reader’s problem	Problem Identification	“CMS for SEO” branches into whether the current CMS is limiting rankings
Comparison	The criteria behind a choice	Solution Research	“Schema markup” branches into JSON-LD versus microdata
Evaluation	Which option fits a specific case	Vendor Evaluation	“Canonical tags” branches into when to use them versus redirects
Implementation	How to execute the decision	Purchase Decision	“Canonical tags” branches into how to implement them
Cost/value	Whether it’s worth doing	Purchase Decision	“SEO agency pricing” branches into case studies, reviews, and guarantees

This buyer-journey mapping framework tells you what a section has to resolve and where in a reader’s decision it’s likely to matter.

Why Query Fan-Out Matters for Your Content Strategy

If your content only answers the initial question a user asks, you’re limiting your search visibility. When your content anticipates and answers the full set of related questions, you can:

• Earn several citations in a single AI Overview.
• Strengthen the topical authority signals that search engines and AI platforms both evaluate.

A Surfer SEO study of 173,902 URLs found this holds at scale. Pages ranking for the main query and at least one fan-out query were 161% more likely to be cited in an AI Overview than pages ranking for the main query alone, and pages ranking only for fan-out queries were 49% more likely to be cited than pages ranking only for the main query. The study is explicit that this is a correlation, and that more than two-thirds of the citations it studied didn’t rank in the organic top 10 for either the main query or any fan-out query.

Use your target audience’s needs to decide which fan-out queries to cover. A tool will surface more sub-questions than any one page should answer, and the buyer-journey stages above are a practical filter.

How AI Systems Use Fan-Out To Build Answers

AI systems treat each section of your content as a potential retrieval unit, or chunk, a self-contained piece that can be retrieved and cited independently. The system decides which of your sections qualify during retrieval, before it writes a word of the answer.

The Typical Query Fan-Out Process

All four steps below happen inside a single response. The expanded queries go out simultaneously, so the whole sequence finishes before the reader sees anything.

1. Intent identification: The large language model (LLM) analyzes the initial question to “understand” what the user wants to know.
2. Query expansion: It generates the related subtopic queries and issues them at the same time.
3. Content retrieval: It runs retrieval against each expanded query and pulls the passages that answer them. This is the step that decides whether your page qualifies for a given subtopic or gets passed over for one that answers it more directly.
4. Response synthesis: It combines these sources into one cohesive answer.

The final response might look like a single paragraph, but it usually stitches together passages from more than one page, each cited for a different subtopic.

A search for “How to use canonical tags” might cite:

• One page that defines canonical tags.
• Another that explains when to use them versus redirects.
• A third that provides step-by-step implementation.

[[EXISTING IMAGE: ai overview example | https://victorious.com/wp-content/uploads/2025/07/ai-overview-example.png]]

In this AI Overview, each hyperlink represents multiple sources.

A query about “What’s the best CMS for SEO?” could cite:

• A definitions page explaining CMS basics.
• A feature comparison chart.
• A technical implementation guide.

Victorious has seen this play out with a real client. After Unionbay lost AI Overview traction, we restructured five high-value pages with summary-first answers and query-intent headings, the same structural principles behind fan-out coverage. Those five pages earned 84 new AI Overview citations in six weeks.

A B2B tech client shows the same mechanism holding up over a longer stretch. After Victorious restructured its content to be more modular and easier for AI systems to extract, its AI Overview citations grew from 26 to 210, a 708% increase, over three months, and the gains held after the initial project ended. Unionbay shows how fast this works. The B2B result shows it keeps working.

How Good Content Can Miss Citation Opportunities

Many web pages target individual keywords without considering the broader questions AI systems retrieve for each query. This approach limits citation potential across AI-generated responses.

Three patterns come up repeatedly in the pages we audit:

1. Stopping at the Surface Question

A page about canonical tags might define the term, but misses opportunities by not covering:

• Why canonical tags matter for SEO.
• When to use canonical tags.
• Implementation steps and best practices.
• Common mistakes and how to avoid them.

Each of these is a separate citation opportunity. A page that stops at the definition competes for one of them, while a page covering the rest of the branch competes for all four, and the model has no reason to reach past the more complete answer.

2. Writing Stories Instead of Creating Modular Resources

Many pages read like opinion pieces with:

• Long introductions that bury key information.
• Missing subheadings that could align with follow-up queries.
• Important answers scattered between long paragraphs.

AI systems retrieve sections, so a page with no visible section boundaries gives them almost nothing to pull.

3. Missing Internal Connections

Even well-written content can lose citation opportunities when it doesn’t link to related content on your site or uses vague anchor text that fails to indicate what the linked content is about. Clear connections help AI models follow the thread of related information across your site’s content.

How To Structure Content for Fan-Out Visibility

Mapping the question journey gives you the branches. Turning them into a page that can be cited for each one is structural work. You need distinct sections that directly answer specific questions and hold up on their own, with formatting that marks where each section starts and stops.

The first two steps set what the page covers and how it divides. The rest are the formatting and linking decisions that make those divisions legible to a model:

1. Map the Question Journey Before Writing

Start with your target query, and use tools like Ahrefs, Semrush, AnswerThePublic, Google’s “People Also Ask,” Google’s “Related searches,” and Search Console to identify common follow-up questions. Organize these into an outline that mirrors a natural user progression.

Before creating anything new, check whether a page you already have answers one of those mapped questions. A gap only exists once you’ve confirmed nothing on your site covers it.

You can also spot-check this directly. Put your target query into ChatGPT, Perplexity, Gemini, or AI Mode and read what subheadings, follow-ups, and sources come back. It’s a live read on what a model is fanning your query out into right now, not a guess based on last quarter’s keyword data.

2. Create Self-Contained Sections

A section passes this test when you can delete every other section on the page and the answer still holds up on its own.

• Use H2s and H3s that match real search phrases.
• Frame headings as questions when possible.
• Lead each section with a direct answer.
• Follow with supporting details, examples, or steps.
• Write each section to be understandable on its own.

3. Connect Related Ideas Strategically

Anchor text is the only part of a link that describes the destination, so “click here” tells a model nothing about what sits on the other side.

• Link to related pages with descriptive anchor text that reflects search intent, like “learn how schema improves rankings.”
• Use breadcrumb navigation to show topic hierarchy.
• Create related content to fill in topical gaps.

4. Add Expandable Content Modules

Expandable content modules make it easier to cover a topic fully without creating walls of text. Include FAQ sections or accordion modules for related questions, and make each answer complete and scannable. The answer text has to be in the page’s HTML when it loads, since content fetched only after a click isn’t there for a crawler to read.

5. Integrate Formatting Elements

Formatting marks where one idea stops and the next begins, which is how a model decides how much text to pull along with a given sentence.

• Use bullet points or lists to logically break up information.
• Use pull quotes or callout boxes to highlight key takeaways.
• Include step-by-step numbered lists for process-oriented content.
• For topics requiring detailed comparisons, use comparison tables with clear column and row headings.

For example:

Feature	Canonical Tags	Redirects
User Experience	No visual impact	Navigates to a new page
SEO Function	Consolidates link equity	Passes authority to new URL

6. Optimize Each Paragraph for Independent Retrieval

AI systems often extract individual paragraphs, so each one needs to make sense on its own:

• Lead with the most important information in each section. If your header is in question form, the first sentence should directly answer the question.
• Avoid pronouns that require context from earlier paragraphs (“this,” “it,” “they”).
• Repeat key subjects and concepts to make each section self-contained.

7. Signal Your Content’s Scope

Scope signals show what else the page covers, so a section retrieved on its own can still lead a model to the related answers nearby.

• Add tables of contents with anchor links to each section.
• Include a summary at the top of your article that describes what you’ll be covering.
• Include Article schema, and add FAQPage schema to any FAQ section. Structured data gives AI systems a machine-readable version of what the page covers and who published it.
• Use “Related content” modules to showcase pieces on the same topic or that are topically relevant.
• Use sidebars or inline sections like, “Questions You Might Also Have” or “Next Steps in Your SEO Journey.”

Structured this way, a page works as both a complete resource for readers and a collection of extractable sections for AI systems.

Build the Branches, Not Just the Root

Since AI search doesn’t stop at the initial query, your content strategy needs to evolve beyond single-keyword targeting to capture this expanded opportunity.

Most of this work is reorganizing pages you already have. Cover more of the branches, and more of the page becomes citable.

This is also where answer engine optimization (AEO) picks up where traditional SEO leaves off. Victorious treats AEO and SEO as one integrated system, and structuring a page so it can be cited on more than one branch is part of that work.

Fan-out is one step in how AI systems find and cite sources. For the full process, see our guide to how AI retrieval works.

To get started, map out the questions your audience asks about your most important topics. Then update and reformat your existing content so it can be cited on more than one branch.

Frequently Asked Questions About Query Fan-Out