Query Fan-Out Optimization: 8 Tactics for LLM Citations

Query fan-out is the process AI search engines use to turn one user question into 5 to 20 simultaneous sub-searches. If your content does not show up across those sub-searches at the passage level, you are invisible in AI-generated answers, regardless of your Google ranking. This is the current standard architecture powering Google AI Mode, ChatGPT Search, Perplexity, Gemini, and Microsoft Copilot, confirmed by Google’s own engineering leadership at Google I/O. [1]

It sounds technical, but the concept is simple once you see it in action. And once you understand it, you will have a completely different picture of how to create content that gets found, cited, and read in the age of AI search.

What Is Query Fan-Out

Imagine you walk up to a librarian and ask:
“What should I do in Nashville with a group of friends?”

A traditional librarian (think: Google before AI) hands you a list of ten web pages and says, “Here, go look.”

An AI librarian does something very different. Before answering you, they quietly send a dozen research assistants in different directions: one to find restaurant reviews, one to look up bars, one to check family-friendly activities, one to compare weekend versus weekday options. They then pull all those answers together into one coherent response.

That dispatching of multiple simultaneous research tasks is query fan-out.

Google’s VP of Search, Robby Stein, explained it plainly:
If you’re asking a question like things to do in Nashville with a group, it may think of a bunch of questions like great restaurants, great bars, things to do if you have kids, and it’ll start Googling basically. 

When you type one question into Google AI Mode, ChatGPT Search, Perplexity, or Gemini, the system automatically breaks that question into multiple smaller sub-queries, fires them all off simultaneously, collects the results, and synthesizes everything into a single answer. The term “fan-out query” was coined publicly by Google’s Head of Search Elizabeth Reid at Google I/O 2025. 

This is now the standard architecture powering every major AI search platform.

Why Query Fan-Out Changes How AI Finds Your Content

Here is the uncomfortable truth: you can rank number one on Google and still be completely invisible to AI search.

That is because AI search does not evaluate your overall page rank. It scans your content at the paragraph level, searching for specific passages that answer specific sub-questions.

The old game was:
“Is my page the most authoritative result for this keyword?”

The new game is:
“Does my content contain self-contained, clearly written passages that answer the many sub-questions an AI might generate from a user’s prompt?”

iPullRank, an SEO research firm, describes it as a shift “from single-search, document-based, to a multi-search, paragraph-based” retrieval model. ^[3]

This is the foundational change behind Answer Engine Optimization (AEO), Generative Engine Optimization (GEO), and Large Language Model Optimization (LLMO), all terms for the practice of optimizing content to appear in AI-generated answers. Understanding query fan-out is the prerequisite for all three.

How Query Fan-Out Works Step-by-Step

Here is what happens behind the scenes in roughly 358 milliseconds every time someone asks an AI search engine a question:

Step 1: Query Analysis.
The AI evaluates the complexity of the question: How much does it already know? Does it need fresh information? Which sub-topics require separate retrieval?

Step 2: Sub-Query Generation.
The system generates multiple related searches. Google’s patent filings identify eight distinct types it generates: Equivalent, Broader, Parallel, Related, Narrower, Personalized, Comparative, and Implicit. ^[4]

Step 3: Parallel Retrieval.
All sub-queries fire simultaneously across the live web, knowledge graphs, and specialized databases, including Google’s Shopping Graph, updated 2 billion times per hour.

Step 4: Chunking.
Retrieved pages are cut into passages of 200 to 500 tokens (roughly 150 to 375 words) and converted into mathematical vectors for semantic comparison. This is where most of your content gets filtered out.

Step 5: Ranking and Filtering.
Passages are scored for relevance, freshness, source authority, and alignment with the original query using Reciprocal Rank Fusion to combine scores across sub-queries.

Step 6: Synthesis and Citation.
The highest-scoring passages from multiple sources are woven into a single answer with citations attached. ^[4]

Perplexity’s own engineering team confirmed this sub-document architecture. Their system retrieves and scores at both the “document and sub-document levels” to surface “the most atomic units possible,” processing 200 million queries per day across an index of 200 billion unique URLs at a median response time of 358 milliseconds. ^[5]

One passage from your page, if it is well-structured, specific, and positioned near the top of the document, can earn a citation even if the rest of the page is mediocre.

What the Research Says About Query Fan-Out Behavior

A number of large-scale studies have tried to quantify how AI search engines behave. Here are the key findings.

How many sub-queries does AI generate?

For simple prompts, ChatGPT generates 4 to 8 sub-queries. For complex prompts, that number rises to 12 to 20. Google AI Mode generates 8 to 12 for a standard query. ^_[6] Surfer SEO’s study of 1,600 test runs found most queries trigger 2 to 5 fan-outs. ^_[7]

The instability problem no one talks about.

Only 27% of fan-out queries stay consistent across repeated runs of the same prompt. A full 66% of fan-out keywords appeared only once across 10 test runs. Only 0.6% of fan-out keywords appeared consistently across all runs. ^[7]

This is the most practically important data point in the research.

You cannot chase individual AI sub-queries the way you chase keywords. The sub-queries themselves keep changing. What stays consistent across runs is the underlying topical cluster. Surfer SEO found roughly 90% of fan-out queries group into 4 main topic clusters per subject. ^[7] Those clusters are stable. That is what you optimize for: the theme, not the specific query.

Where AI citations appear on your page.

Two independent studies found the same positional bias. An analysis of 1.2 million ChatGPT responses found 44.2% of citations come from the first 30% of a page. ^[8] A separate study of 42,971 citations found 75% of cited sentences were in the first 50% of the page. ^[9] This is not a writing best practice. It is how transformer-based language models process text. Passages at the top of a document receive more attention weight in the architecture.

What content formats get cited most.

Comparative listicles account for 32.5% of all AI citations, the highest of any format tested. Comparison tables with proper HTML markup show 47% higher AI citation rates. ^[10] FAQ schema pages are 60% more likely to appear in AI-generated answers. ^[11] Question-format headings correlate with approximately 2x more citations. ^[8]

The Princeton and IIT Delhi GEO study, based on 10,000 queries, found that adding citations to content boosted AI visibility by 115.1% for lower-ranked sites. Adding statistics boosted it by 22%. Keyword stuffing had a negative impact. ^[10]

The language bias in fan-out queries.

Peec AI analyzed over 10 million prompts and found 43% of ChatGPT’s background search queries run in English even when the original prompt is in another language. For Turkish-language prompts, English fan-out queries fired 94% of the time. ^[14]

What Happens to Your Traffic When AI Summaries Appear

The volume of traditional organic clicks is already declining. OWDT reports that when an AI summary appears in search results, only 8% of visits include a click on a traditional organic result, compared to 15% without an AI summary. ^_[15]

The offset is visit quality. Semrush found the average AI search visitor is 4.4 times more valuable than a traditional organic visitor, and ChatGPT-referred traffic converts at roughly 30% for some clients. ¹⁶⁶AI answers function as a qualification filter. Users who click through to a cited source have already received context about that source from the AI system. They arrive with more intent and more trust.

Actionable Tactics for Query Fan-Out Optimization

1. Front-load your best content.

Given that 44.2% of AI citations come from the first 30% of a page ^[8], the traditional approach of building toward a conclusion actively works against you in AI search.

Put your most important content near the top. Lead with your core claim. Lead with your most important statistic. Lead with your clearest definition or recommendation.

Your action: Go to your most important pages. What is in the first 30% of the content? If it is mostly introduction and background, restructure so the best material appears first.

2. Add statistics and citations.

The Princeton and IIT Delhi GEO study found that adding citations to content boosted AI visibility by 115.1% for lower-ranked sites, and adding statistics boosted it by 22%. ^[10]

Your action: For every major claim in your content, ask: Can I add a specific statistic here? Can I cite a credible source? Structured, evidence-based content is more citable.

3. Use question-format headings throughout.

Question headings align directly with sub-queries AI systems generate during fan-out. They also correlate with 2x more citations in citation pattern research. ^[8] “What Is Query Fan-Out” outperforms “Understanding Query Fan-Out” as a heading for AI retrieval.

Your action: Audit your heading structure. Convert statement-format headings to question-format headings where it fits naturally. AEO and LLMO both reward this structure.

4. Build brand presence across multiple platforms.

Since brand search volume is the strongest predictor of AI citations, and sites on 4 or more platforms are 2.8 times more likely to appear in ChatGPT responses ^[10], distributing your brand signal across the web matters more than chasing backlinks.

Wikipedia accounts for 47.9% of ChatGPT’s citation sources. ^[13] Reddit is Perplexity’s top citation source at 46.7%. ^[10] A Wikidata entity entry feeds directly into Google’s Knowledge Graph.

Your action: Make sure your brand has a verifiable presence on Wikipedia (if you qualify), Reddit (participate in relevant communities authentically), Wikidata, G2 or Capterra if applicable, and LinkedIn. These are the platforms AI systems draw from most consistently.

5. Update your content regularly.

AI systems cite content that is 25.7% fresher on average than what traditional search surfaces. ^[4] The Digital Bloom found 65% of AI crawler hits target content published within the past year. ^[10]

Your action: Audit your most important content. If it has not been updated in 6 or more months, refresh it. Add new statistics, update examples, revisit conclusions. Even a modest update resets freshness signals for AI crawlers including GPTBot, ClaudeBot, and PerplexityBot.

How to Find the Fan-Out Queries for Your Topic

You can see exactly what sub-queries AI systems generate using four methods.

For advanced LLM users: Perplexity Steps tab.
Search your topic in Perplexity and click the Steps tab. It shows every background query the system ran before generating the answer. Run the same prompt at least 10 times across multiple sessions. Record every sub-query. Ignore the ones that appear once. Identify the 3 to 4 topical themes that recur consistently. Those recurring themes are your actual content gaps. Not the specific queries, but the angles your content is failing to cover.

The easy way for novice users: Aided’s Fan-Out Query app.
Just type in your topic and the LLM will reverse engineer the fan-out. You will then be given a Fan Query brief with everything you need to optimize your content for maximum LLM citations. 

A controlled Semrush experiment applying fan-out research to 4 blog articles increased total AI citations from 2 to 5 in one month, a 150% increase. ^[2]

The Bottom Line on Query Fan-Out

Query fan-out is not a passing trend. It is the fundamental architecture of how AI search engines retrieve information, confirmed by Google’s own public statements, patent filings, and observable behavior across Google AI Mode, ChatGPT Search, Perplexity, Gemini, and Microsoft Copilot.

The core shift is that you are no longer being ranked for pages. You are being evaluated for passages.

Content that covers multiple facets of a topic with clear self-contained answers in each section, supporting statistics and citations, structured schema markup, and regular freshness updates will earn AI visibility. Content built purely around keyword density and domain authority will increasingly fade from AI-generated results.

The good news is that these changes reward genuinely helpful, well-structured, authoritative content. For creators who focus on quality, this is an opportunity to compete against larger, less careful domains in a way traditional SEO rarely allows.

Reference List

[1] Query Fan-Out Technique in AI Mode: New Details From Google
https://www.searchenginejournal.com/query-fan-out-technique-in-ai-mode-new-details-from-google/552532/

[2] What Is Query Fan-Out and Why Does It Matter?
https://www.semrush.com/blog/query-fan-out/

[3] How AI Search Platforms Expand Queries with Fan-Out and Why It Skews Intent https://ipullrank.com/expanding-queries-with-fanout

[4] Query Fan-Out: A Misunderstood Concept in AEO and SEO
https://higoodie.com/blog/query-fan-out

[5] Architecting and Evaluating an AI-First Search API
https://research.perplexity.ai/articles/architecting-and-evaluating-an-ai-first-search-api

[6] What Is Query Fan-Out? How One Query Becomes 12 in AI Search
https://www.ekamoira.com/blog/query-fan-out-original-research-on-how-ai-search-multiplies-every-query-and-why-most-brands-are-invisible

[7] AI Search Study: Understanding Keyword Query Fan-out
https://surferseo.com/blog/keyword-query-fan-out-research/

[8] What 1.2 Million ChatGPT Responses Actually Reveal About LLM Citation Patterns
https://victorinollc.com/thinking/llm-citation-attention-patterns

[9] Answer Engine Optimization: How to Optimize Content for LLM Citations
https://www.annsmarty.com/p/answer-engine-optimization-how-to

[10] 2025 AI Visibility Report: How LLMs Choose What Sources to Mention
https://thedigitalbloom.com/learn/2025-ai-citation-llm-visibility-report/

[11] What Is Query Fan-Out? A Beginner’s Guide to Google’s AI Search Engine (2026)
https://linksurge.jp/blog/en/query-fan-out-guide-2026/

[12] Comparative Analysis of LLM Citation Behavior: SEO Strategy Implications
https://searchatlas.com/blog/comparative-analysis-of-llm-citation-behavior/

[13] LLM Citation Trends That Matter in AI Search
https://wellows.com/blog/llm-citation-trends-for-ai-search/

[14] ChatGPT Search Often Switches to English in Fan-Out Queries: Report
https://www.searchenginejournal.com/chatgpt-search-often-switches-to-english-in-fan-out-queries-report/567811/

[15] What Is Answer Engine Optimization? How AEO Changed SEO
https://owdt.com/article/what-is-answer-engine-optimization/

[16] LLM Optimization (LLMO): Get AI to Talk About Your Brand
https://www.semrush.com/blog/llm-optimization/

[17] LLM SEO: The Complete Guide to Large Language Model Optimization (2026)
https://llmrefs.com/blog/llm-seo-optimization[18] LLM Citation Tracking: How AI Systems Choose Sources (2026 Research) https://www.ekamoira.com/blog/ai-citations-llm-sources