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How Query Fan-Out Works? (and Why One Keyword Isn’t Enough)

How query fan-out works?

Summarise this blog post with:

ChatGPT
Gemini
Claude
Grok

Table of Contents

Key Takeaways
  • Fan-out query refers to how the AI search engines will split a user query into several sub-queries aimed at a particular facet of the original question before synthesizing the results for the final answer. Google AI Mode will run between 5 and 11 sub-queries on one prompt while ChatGPT Deep Research ran 420 sub-queries on one query.
  • Over 95% of fan-out sub-queries do not recur in any other searches. This implies that keyword research tools will never identify them and optimizing for the keywords will only cover less than half of the search surface used by the AI.
  • The AI does not look for the exact phrase typed by the user. Rather, the AI creates sub-queries synthetically depending on the inferred meaning of the query and other possible follow-up queries. The ranking of the original keyword is simply the start of citation eligibility.
  • Research found that ranking for several fan-out sub-queries increased the chances of citation significantly. Coverage of a topic cluster is now more effective than optimizing a single page for a single head keyword.
  • The practical response is to map your topic's fan-out patterns, audit your cluster for coverage gaps, close those gaps with content and structured data, and measure citation performance separately from organic rankings.

You built the content. You earned the ranking. You hit position one. Then Google AI Mode answered the question without naming your page once. The reason is query fan-out: the mechanism that turns one user query into 5, 10, or even hundreds of parallel searches before an AI answer is generated. Understanding it is now the most important concept in AI search optimisation. This post explains exactly how it works, what sub-queries look like in practice, and how to audit your content to compete across the full retrieval surface.

While traditional SEO was one to one: one query, one intent, one top-ranking content piece, AI Mode is a one-to-many SEO strategy. After the user submits their question, multiple sub-queries are launched to explore all the sides of the initial query. Then, Gemini combines the results from each of the queries into one output. While the site ranking for the head keyword wins just one spot out of many, the sites answering the adjacent sub-queries win the rest. When you have optimised your site for just the head keyword, you are only fighting for the smaller part of the citation surface.

What Is Query Fan-Out?

The query fan-out is the process used by the artificial intelligence search engines to break down one user's query into multiple sub-queries, find answers to each of them separately and synthesise the final result. The process was explained in the patent documents of Google and became the basis of how AI Mode, AI Overviews, ChatGPT and Perplexity work now. Instead of trying to find the answer to the exact query the user asked, the artificial intelligence produces synthetic sub-queries based on what an informed person would need to know to fully answer the original question.

Research found that Google AI Mode runs an average of 9 to 11 sub-queries per prompt, with 59% of prompts triggering between 5 and 11 searches and 24% triggering 12 to 19. The maximum observed was 28. ChatGPT's Deep Research feature ran 420 sub-queries for a single prompt and cited 30 sources, running over 200 of those searches in its first step alone just to resolve the user's phone model and case-type assumptions before it could even begin answering. The scale varies by platform and query complexity, but the underlying logic is consistent: the AI is not searching once. It is searching many times, from many angles, simultaneously.

9 to 11 Average sub-queries per Google AI Mode prompt. 59% of prompts trigger 5 to 11 searches.
95%+ of fan-out sub-queries have zero recurring search volume and will never appear in keyword tools

The 95% zero-volume point is the biggest takeaway from the implications above for content strategy. The sub-queries created by the AI Mode are synthetic: made based on the inference of the intended meaning, context and potential follow-ups, not based on the history of the searches done by the actual users. This means they are completely invisible to any existing keyword tool. If your keyword strategy is geared towards finding keywords with measurable search volume, you are focusing only on a tiny fraction of queries while overlooking a much bigger surface of synthetic queries created by the AI on the spot to formulate its answer.

1 The Mechanism

How Sub-Queries Are Generated

How query fan-out works in Google AI Mode

Based on analysis of Google's patents, fan-out sub-queries obey certain patterns. They are predictable, not random. The depth of the fan-out is driven by how much context is missing from the original query: the more dimensions left unresolved, the more sub-queries the AI generates to fill in the gaps before it can construct a confident answer. A query like "best CRM for Malaysian SME" is underspecified across at least four dimensions simultaneously: what size of SME, what industry, what budget, and what features matter most. Each unresolved dimension can trigger its own sub-query. Understanding those patterns is what makes it possible to audit and close coverage gaps systematically.

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Disambiguation
AI tests various interpretations of the ambiguous query. "Best red phone case" triggers sub-queries for different phone models, price tiers, and use cases before the AI settles on one interpretation. If your content addresses only one interpretation, you are invisible to every other sub-query the AI runs for the same head term.
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Entity Attributes
For any named product, brand, or concept, the AI generates sub-queries for its specific attributes: specifications, pricing, comparisons, reviews, and availability. A page that names a product but does not cover its attributes completely will be outranked in those attribute sub-queries by a page that does, even if the latter ranks lower overall.
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Journey Stages
A single query can trigger sub-queries across multiple purchase stages simultaneously: awareness ("what is X"), consideration ("X vs Y"), and decision ("where to buy X in Malaysia"). Content covering only one stage competes for a fraction of the available citation slots. A topic cluster with dedicated pages per stage covers all of them.
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Comparisons
AI Mode generates comparison sub-queries even when the user did not ask for one. A query about a product will likely trigger a sub-query comparing it against its two or three closest named alternatives. If you have no comparison content, a competitor's comparison page earns the citation slot while your product page earns nothing from that sub-query.
📅
Recency
Queries with temporal relevance generate sub-queries filtered by recency. The AI has been observed running sub-queries like "top rated phones February 2026" even when the user typed only "best phone". Content that has not been updated recently loses citation slots on these sub-queries to fresher sources regardless of domain authority or historical ranking position.
✅
Trust Signals
For YMYL queries, the AI generates sub-queries specifically targeting credibility: qualifications, regulatory compliance, and expert endorsements. A page without visible author credentials, cited sources, or third-party validation will be passed over on trust sub-queries even if it ranks well for the head term. E-E-A-T signals are not decorative for these query types: they are citation criteria.

The effect on the practical application level is that two queries that may appear similar can lead to entirely different fan-outs. A query on "best noise-cancelling headphones" will result in sub-queries related to attributes and comparisons. A query on "how to choose noise-cancelling headphones" will result in sub-queries related to journey stage and trust signals. The same page cannot win a citation slot in both cases unless it has been prepared to address the entire topic cluster.

📌 The AI does not look for the exact phrase that was typed by the user. Sub-queries are created based on intent inference. When you optimise your page for the exact match keyword, you optimise for the one sub-query that the AI is least likely to run verbatim.
2 The Scoring

How AI Mode Selects What to Cite

Once sub-queries have retrieved results, Gemini uses a process called reciprocal rank fusion (RRF) to combine and score sources across multiple retrieval lists. Each source earns a score based on its ranking position across the sub-queries it appeared in. A source ranked second in one sub-query and fifth in another earns a combined score of 1/2 + 1/5 = 0.7. The final citation selection is a re-ranking of these combined scores.

This has one critical implication: a page that ranks moderately across several related sub-queries can outscore a page that ranks first for only one of them. The maths rewards coverage over dominance. A page at position 3 across four sub-queries (combined RRF score of 4 x 0.33 = 1.33) outscores a page at position 1 for a single sub-query (RRF score of 1.0). Breadth of sub-query coverage is not just a nice-to-have. It is a scoring input that directly affects whether your content gets cited.

Single Keyword Strategy Page ranks #1 for the head keyword. Earns an RRF score of 1.0 for that sub-query. Scores zero on all adjacent sub-queries. Total combined score: 1.0.
Cluster Coverage Strategy Page ranks #3 across four related sub-queries. Earns 0.33 per sub-query. Total combined score: 1.33. Cited above the #1-ranked single-keyword page.

Studies found that ranking for multiple fan-out sub-queries raises citation probability significantly. The implication is not that ranking first no longer matters. It is that ranking first for a single keyword while ignoring the surrounding sub-query landscape is now a structurally losing strategy in AI search.

3 The Strategy

Four Steps to Optimise for Query Fan-Out

The fan-out optimisation framework follows four stages. Each builds on the previous one, and skipping any of them leaves identifiable gaps in your citation coverage.

Four steps to optimise for query fan-out
  • 01 🗺️
    Discover

    Map Your Fan-Out Patterns

    Submit your target query to AI Mode and read the response section by section. Each section corresponds to a sub-query that fired. Note the heading, the source cited, and what type of question that section answers. A query about "how to start a podcast" fires sub-queries like "podcast recording equipment" and "best podcast hosting and distribution 2025", alongside branding, guest outreach, content planning, and promotion, all in parallel before any answer is produced. Each section heading is effectively the sub-query the AI ran. List them, then categorise each one against the six pattern types: disambiguation, entity attributes, journey stages, comparisons, recency, and trust signals. The result is your fan-out map for that topic.

  • 02 🔍
    Audit

    Audit Your Cluster Coverage Against These Patterns

    Chart all pages and sections of your existing content against the fan-out patterns you identified. Mark each sub-query as covered, partially covered, or missing. "Covered" means a section with a heading and a direct answering statement. "Partially covered" means the information exists but is buried or unclear. "Missing" means neither your site nor any page you link to addresses that sub-query. The missing slots are exactly where competitors are earning citations you are not.

  • 03 🛠️
    Execute

    Fill Coverage Gaps On-Site and Off-Site

    On-site: write sections for partially covered sub-queries, build cluster pages for distinct missing intents, and complete your schema markup for entity-heavy patterns. Off-site: identify which third-party sources AI Mode is citing for the sub-queries you are not covering (review sites, listicles, comparison pages) and pursue those as link and mention targets.

  • 04 📊
    Measure

    Measure Citation Performance Separately from Organic Rankings

    Employ AI SEO performance metrics that are separate from standard rank tracking. Use the Generative AI search type filter in the Performance tab of Google Search Console to see which pages generate AI impressions. Track topic clusters in aggregate with tagged rank tracking rather than monitoring individual keywords. Monitor brand citations across AI platforms to identify which sub-query patterns name you and which name competitors. Shift your success metric from per-keyword position toward topic-level visibility and citation frequency. Your standard organic rank report will not capture any of this.

Fan-Out Pattern Types and How to Cover Them

Fan-out pattern types in AI search

Before mapping your coverage, it helps to understand how fan-out sub-queries are formatted. Research identifies seven common sub-query formats the AI generates from a single prompt: related topics (expanding the core topic into adjacent areas), implicit questions (answering what the user implied but did not ask), comparative (evaluating options side by side), recency (filtering for the most current information), reformulations (rephrasing the same intent differently), contextual variations (location-based or behavioural variants), and next-step queries (what the user will need to know after this answer). A single prompt can trigger all seven formats simultaneously. The six function patterns below describe what the AI is trying to accomplish with those sub-queries.

  • Entity Attributes: AI searches for the specific attributes of a named product, brand, or concept: colour, material, features, compatibility, specifications, price range, and availability. Cover this with complete attribute data on product and service pages, descriptive image filenames and alt text, collection pages for key attribute combinations, and complete product schema markup.
  • Journey Stages: AI searches for information at every stage of the buying process from awareness to decision. Cover this with a topic cluster that has a dedicated page or section per stage: awareness, education, comparison, decision, and implementation, all connected through internal linking.
  • Trust Signals: AI searches for credibility signals, expert opinion, compliance, and validation, particularly for YMYL and high-cost decisions. Cover this with visible author credentials, third-party citations, published policies, reviews, awards, and case studies.
  • Comparison Criteria: AI searches for comparisons of similar products or services and identifies which attributes drive decisions. Cover this with dedicated comparison pages or sections that evaluate options side by side against the criteria your audience actually uses.
  • Action and Risk: AI searches for post-decision details including availability, shipping, returns, warranties, and refunds. Cover this with clear policy pages and product-level logistics information that answers what happens after the purchase decision is made.
  • Recency: AI searches for the newest available information on time-sensitive topics such as pricing, regulations, and product launches. Cover this with regularly refreshed content and a visible last-updated timestamp in both on-page text and structured data.
💡 In most cases, a topic will trigger multiple pattern types. Searching for legal services for a Malaysian SME is a query that activates entity-centric, trust-centric, and journey-centric sub-queries all at once. Run a pattern audit on your topic cluster against all pattern types, not just the most obvious one.

Frequently Asked Questions

No. Query fan-out is used by all major AI search platforms, including ChatGPT, Perplexity, and Gemini. The scale and depth vary: Google AI Mode typically runs 9 to 11 sub-queries per prompt, while ChatGPT's Deep Research has been observed running 420 sub-queries for a single query. The underlying mechanism is the same across platforms. Content strategy built around topical cluster coverage and sub-query pattern mapping improves citation eligibility across all of them, not just Google.
You find them by working from the pattern types rather than from keyword data. Submit your primary topic query to AI Mode and analyse what the response implies: what sub-topics does it cover, what sources does it cite per section, and what follow-up questions does it raise? Each of those represents a likely sub-query. Supplement this by testing related queries and competitor pages in AI Mode to see which adjacent questions are being answered. Keyword research tools are a starting point for head term identification, not a map of the full sub-query landscape.
Ranking helps, but it is not the only path to citation. Pages outside the top 10 can be cited when a specific passage directly answers a sub-query that higher-ranked pages do not address clearly. The AI is not re-ranking your page: it is retrieving the passage. A page at position 15 with one precisely written section on a niche sub-query can earn a citation slot that a position-one page misses entirely because it never covered that sub-query. The combination of ranking position, passage clarity, and sub-query specificity together determine citation eligibility, not ranking position alone.
Traditional Google ranking scores a page against a single query and returns a ranked list. Reciprocal rank fusion scores a source across multiple retrieval lists, one per sub-query, and combines the scores. A page that appears in four sub-query results at moderate positions can outscore a page that ranks first for only one sub-query. This is a structural shift: it rewards breadth of relevance across a topic cluster, not just depth of optimisation for a single keyword. The practical implication is that your content's citation probability is determined by how many sub-queries it shows up in, not just where it ranks for the head term.
Not necessarily. The decision rule is: one page per distinct intent, where distinct means a genuinely different user goal, a different evidence set, or a different decision it supports. Sub-queries that represent different facets of the same intent belong as sections within one comprehensive page. Sub-queries that represent a different stage of the user journey, a different comparison axis, or a different use case warrant a separate page. Mass-producing slight keyword variations as separate URLs is both ineffective against fan-out and a risk under Google's scaled content abuse policy, which targets pages made primarily to manipulate rankings through keyword variation rather than to serve distinct user needs.
Use Google Search Console's Generative AI search type filter under the Performance report to monitor AI impressions and clicks by page. This shows which pages are generating AI citations and from which queries. Combine this with manual testing: submit your target sub-queries to AI Mode and track whether your pages appear in the cited sources over time. For a broader view of brand citation across AI platforms, tools that track AI-generated mentions can show you which sub-query patterns are naming you and which are naming competitors. Organic rank reports alone will not capture this data.

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Let AI SEO Agency map your query fan-out patterns, identify your sub-query coverage gaps, and build the content cluster that earns citations across AI Mode, AI Overviews, and Gemini.

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Jiayi G.

Jiayi Gan is the CMO at Marvant Evolutions, with experience spanning startups & leading 4A MNC advertising agencies. She has managed multi-million-ringgit advertising investments across Google, Meta, and TikTok for businesses across multiple industries. Today, she leads AI-powered marketing initiatives across SEO, AIGC, marketing automation, and performance media, enabling brands to improve search visibility, scale content production, and accelerate business growth.

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