You can hold the top organic spot for your money keyword and still be completely absent from the AI answer above it. That’s not a bug. It’s the predictable outcome of how AI search retrieves information.
The mechanism behind it is query fan-out: the process where an AI system takes one prompt, silently explodes it into a set of narrower sub-queries, runs them in parallel, and synthesizes the results into a single answer.
Backlinko’s primer on query fan-out makes the core point well — coverage and retrievability beat rank position. This guide goes further: the patent evidence, the retrieval pipeline stage by stage, what the data actually supports (and what it doesn’t), how to observe fan-out yourself, and a repeatable framework for acting on it.
What Query Fan-Out Actually Is
Query fan-out is query expansion at retrieval time, executed in parallel, by a language model.
Instead of matching your prompt against an index once, the system decides what it needs to know in order to answer well, writes those information needs as separate searches, fires them simultaneously, and then assembles an answer from the passages it gets back.
Search Engine Land notes that most AI-powered search engines — including AI Overviews, AI Mode, Gemini, ChatGPT, Perplexity, Microsoft Copilot, and Grok — use query fan-out expansion methods to answer user prompts.
The key shift: the query the user typed is often not the query your page competes for. You’re competing for the sub-queries the model invented on the user’s behalf.
The user never sees these questions — they only see the final answer. But they influence which sources the AI uses to build that answer.
The Patent Trail: Where This Came From
Fan-out isn’t marketing vocabulary invented in 2025. It has a documented lineage, and reading it tells you what the system optimizes for.
US11663201B2 — ”Generating query variants using a trained generative model”
This patent describes a system that takes a single search query and generates multiple related query variants using a trained generative model. Each variant is issued separately, and the combined results are used to produce a final response. Google never calls this ”query fan-out” in the patent — the formal term is ”query variant generation”, but the behavior is identical.
Two details matter for strategy: the system uses reinforcement learning to determine which variants are most productive for a given question type, and it can select different generative models based on the user’s location, predicted task, and even time of day. Fan-out is contextual, not a fixed keyword list.
US20240289407A1 — ”Search with Stateful Chat”
Published August 29, 2024, this describes the broader conversational search pipeline. It covers a system that uses large language models to generate multiple alternate queries from the original search, beginning with what Google calls ”prompted expansion” — structured instructions given to a model telling it how to branch.
US12158907B1 — ”Thematic Search”
Filed December 2024, this one is the most useful for content teams. The system organizes search results into themes and provides AI-generated summaries for each. A single query can produce multiple sub-queries based on ”sub-themes” — searching ”moving to Denver” might generate themes like neighborhoods, cost of living, things to do, and pros and cons.
And here’s the line that should reshape how you brief writers: your content might appear not because it comprehensively covers the main topic, but because it provides the best information for one specific sub-theme.
Standard caveat: virtually all Google patents list multiple possible implementations, and Google generally does not confirm that a patented invention is in use. Treat patents as a map of intent, not a spec sheet.
The Fan-Out Pipeline, Stage by Stage
Most explainers stop at ”one query becomes many.” The operationally useful part is what happens at each stage, because you can lose at any one of them.
- Intent parsing. The model reads the prompt plus session context — prior turns, location, inferred task.
- Sub-query generation. The model writes N narrower searches covering different facets. In AI Mode, the system decomposes the query into themed subqueries and fires them in parallel across the web and Google’s internal graphs — Knowledge Graph, Shopping Graph, Maps — then synthesizes a cited response.
- Parallel retrieval. Each sub-query returns its own candidate set. Your page has to be findable for the sub-query, not the head term.
- Passage selection. The system extracts specific chunks, not whole pages. A 3,000-word article competes at the paragraph level.
- Reranking and consolidation. Duplicate claims collapse. Conflicting claims trigger cross-checks against more sources.
- Synthesis and citation. The final answer is written, and only a fraction of retrieved sources earn a visible link.
Ranking well gets you into step 3 for one sub-query. Winning citation requires clearing steps 4, 5, and 6 as well.
What the Data Actually Shows
Two findings dominate the evidence base, and both come from the same December 2025 Surfer SEO study.
The research examined the top 10 ranking pages for 10,000 keywords, found 76% triggered AI Overviews, used Gemini to extract 33,000 fan-out queries, then scraped the top 10 organic results for those fan-outs as well.
The headline results:
- A strong correlation (Spearman 0.77) between how many fan-out queries a page ranks for and its likelihood of being cited in AI Overviews.
- Pages ranking for fan-out queries are 161% more likely to be cited than pages ranking only for the main query.
- Pages ranking for both the main query and at least one fan-out accounted for 51% of AI Overview citations; pages ranking only for the main query accounted for just under 20%.
- 68% of pages cited in AI Overviews were not in the top ten organic results.
On the scale of fan-out itself, larger-sample analyses point in a consistent direction. Data covering 72,000+ AI-generated queries and 8,700+ prompts found that a single question to ChatGPT or Gemini routinely triggers 8–10 parallel, hyper-specific queries before an answer is returned.
And the shape of those queries is the real story: roughly 95% of fan-out phrases show zero monthly search volume, and average fan-out query length runs about 5.5 words in ChatGPT and 9.1 words in Gemini, versus roughly 3.4 words for classic Google searches.
Translation: your keyword tool cannot see the queries that decide your AI visibility.
The Honest Caveat Most Articles Skip
The 161% figure is a correlation measured after the fact. It’s worth holding it at arm’s length.
As one skeptical analysis puts it: these are measurements of what cited pages look like, taken after the fact. A page cited by an AI Overview will, on inspection, tend to rank for many related queries — in the same way that profitable companies tend to have large finance teams. Coverage is what winning looks like from the outside; it is not necessarily the mechanism that produces the win.
That’s a fair critique, and it changes the prescription in a useful way. Don’t publish 40 thin pages to ”cover fan-outs.” Coverage without substance is cargo-culting the correlation. Surfer’s own recommendation lands in the same place: don’t chase fan-out queries — own the topic, by building comprehensive topical coverage and publishing content that addresses a wide range of related questions.
Fan-Out Behaves Differently on Every Platform
Google AI Mode and AI Overviews
The most aggressive implementation. Google has talked openly about firing ”hundreds of searches” and organizing results by theme, and it retrieves from proprietary graphs alongside the open web. Because fan-out operates across the live web, the Knowledge Graph, structured data, and shopping results simultaneously, content appearing in featured snippets, product feeds, and knowledge panels all contributes to citation probability.
Perplexity
The transparent one. Perplexity openly shows the subqueries it uses and the sources it draws on. It displays its search steps as it works, so you can read its fan-out without any tool at all. Use it as your free research lab.
ChatGPT Search
Opaque, and getting more so. ChatGPT breaks a question into narrower searches before it retrieves, but hides the sub-queries it ran — so the workaround is to read the sources it cited and work backward to the questions those pages answer, then check whether you own a page for each. Notably, ChatGPT removed public fan-out data, and prompt trackers that relied on exposed fan-out queries lost that signal overnight.
Why chasing exact wording is a trap
Fan-out queries are LLM-generated, which means they’re probabilistic, not deterministic — run the same query twice and you’ll get different sub-queries. Backlinko cites the same instability: only 27% of fan-out sub-queries remain consistent across repeated searches.
But the themes converge even when the specific wording doesn’t — fan-out queries for ”best CRM software” will reliably include pricing comparisons, integration lists, and reviews.
Optimize for the themes. Ignore the strings.
How to Actually See Fan-Out Queries
Four methods, roughly in order of effort:
1. Read Perplexity’s steps (free, 30 seconds)
Ask your buyer’s real question and watch the search steps render. Log every sub-query. Do this for your 20 highest-value prompts and you have a themed map in an afternoon.
2. Browser DevTools (free, higher fidelity)
The Network tab in Chrome or Firefox DevTools logs every request your browser sends. When you type a query into an AI search tool, those requests often contain the sub-queries the LLM generates as part of fan-out — filtering and inspecting them shows what the model is actually searching for. Open DevTools before you type, confirm recording is on, then submit a moderately complex prompt.
3. Dedicated tooling (scale)
Ahrefs surfaces this natively: fan-out queries appear in Brand Radar under AI Responses, in a Fanout queries column, currently supported for ChatGPT and Perplexity. Platforms like Profound track query fan-outs across AI search engines at scale, letting you see patterns across hundreds of prompts. Simulators built on the Thematic Search patent are a reasonable proxy when live data isn’t available.
4. Server logs (ground truth on retrieval)
Fan-out tools tell you what was asked; logs tell you what was fetched. Crucially, learn to separate two crawler classes: training crawlers such as GPTBot, ClaudeBot, CCBot, and Google-Extended collect content for model development and appear sporadically rather than continuously, while retrieval crawlers such as ChatGPT-User and PerplexityBot support live answers, are event-driven, and often fetch only a small number of URLs in response to a specific prompt.
A spike in retrieval crawler hits on a URL is the closest thing you have to a real-time signal that fan-out is reaching your page.
A Six-Step Framework for Fan-Out Optimization
Step 1: Map themes, not keywords
Take your 15–25 highest-intent buyer prompts. For each, collect the observed sub-queries from Perplexity and DevTools. Cluster them into themes: definition, comparison, pricing, integrations, risks/limitations, alternatives, implementation, proof.
You’ll usually find 6–10 recurring themes per topic. That’s your coverage target.
Step 2: Audit which themes you already own
For each theme, ask: do I have a page — or a clearly delineated section — that a retrieval system could lift as a standalone answer? Mark each theme as owned, buried, or missing.
”Buried” is the most common and the cheapest to fix.
Step 3: Decide consolidate vs. create
Rule of thumb: if a theme is a genuine standalone information need with its own vocabulary (”pricing,” ”alternatives,” ”vs. competitor”), give it a page. If it’s a facet of an existing decision, give it an H2 with a self-contained answer on the page that already ranks.
Creating thin pages per sub-query is the failure mode. Depth beats sprawl.
Step 4: Restructure for passage-level extraction
This is where most of the winnable ground is. AI systems scan your content and synthesize the exact passage that resolves a query — so the unit of competition is the chunk.
- Front-load the answer. First 1–2 sentences under each heading should stand alone if copy-pasted with no context.
- Use literal, descriptive H2/H3s. ”How much does X cost?” outperforms ”Investment Considerations.”
- Kill anaphora. ”This approach” and ”as mentioned above” break a chunk when it’s extracted alone. Repeat the entity name.
- One claim per paragraph. Merged claims are harder to attribute cleanly.
- Add comparative and qualifying language where it’s genuinely true. Including ”best” and comparative terms in titles, headings, and body copy helps models recognize a page as relevant for comparison intent — but only if the page actually delivers a comparison.
Step 5: Put your best material early
Placement measurably matters. Backlinko highlights Kevin Indig’s analysis of 1.2 million ChatGPT responses: 44.2% of citations in ChatGPT responses come from the first 30% of a page, 31.1% from the middle, and 24.7% from the final third.
Practical implication: stop hiding your differentiated data, pricing, and direct answers below 800 words of throat-clearing.
Step 6: Extend coverage off your own domain
Fan-out retrieves from wherever the best answer lives. If a sub-query is ”what do users say about [product],” your product page will never win it — a review platform, a community thread, or an independent roundup will.
Audit your themes for the ones you structurally cannot own, then invest in being present and accurately represented on the sources that do: review sites, comparison directories, industry data sources, Wikipedia-adjacent reference material, and expert roundups.
How to Measure Fan-Out Performance
Rank tracking alone will mislead you here. Build a dashboard around four layers:
- Theme coverage rate. % of mapped sub-themes where you own a retrievable passage. This is your leading indicator.
- Fan-out ranking breadth. Number of extracted fan-out queries your URL ranks in the top 10 for — the variable Surfer’s correlation is built on.
- Retrieval evidence. Retrieval-crawler hits per URL from server logs.
- Citation share. % of tracked prompts where your domain appears, plus which specific URL got cited.
The gap most teams miss: your AEO tracking tool watches the prompt you typed, not the sub-queries the model ran — and that gap is why pages that ”rank” for a tracked prompt still get left out of the answer. The branches only show up in your data after you load the narrower searches yourself as separate prompts.
So do exactly that: promote your top 30 recurring sub-queries into tracked prompts of their own.
Five Mistakes That Waste Fan-Out Budget
- Treating fan-out queries as keywords. They have no volume, they change every run, and exact-matching them produces awkward, low-value content.
- Publishing one thin page per sub-query. This mimics the correlation without producing the substance that earns selection.
- Assuming rank 1 equals citation. ChatGPT cites pages in position 21+ almost 90% of the time, according to Semrush data.
- Ignoring the off-domain layer. A large share of sub-queries resolve to third-party sources by design.
- Blocking retrieval crawlers by accident. Check robots.txt, WAF rules, and bot-management settings. If ChatGPT-User and PerplexityBot can’t fetch you, nothing else you do matters.
Where This Is Heading
Two trajectories are worth planning against.
Fan-out gets bigger and more predictive. Systems are expected to generate fan-out sub-queries before users finish typing, pre-fetching and synthesizing answers in anticipation — creating near-zero-latency answers and further increasing the importance of comprehensive coverage.
Fan-out becomes agentic. AI agents are expected to run multi-step workflows — compare options, plan, and complete actions like booking. That elevates structured data, machine-readable availability and pricing, and API-accessible content from nice-to-have to prerequisite.
The Bottom Line
Backlinko’s framing is correct as far as it goes: in AI search, coverage and retrievability outrank rank position. The fuller picture is that fan-out is a multi-stage filter, and you need to survive all of it.
You need to be retrievable for the sub-queries (theme coverage), extractable at the passage level (structure), credible enough to survive reranking (evidence, specificity, expertise), and present on the third-party sources that own the sub-queries you can’t.
The good news is that this is not a big-brand-only game. Smaller sites can still be cited if they are clear, specific, and trustworthy — well-structured niche pages that answer sub-questions directly often earn visibility that their domain authority alone wouldn’t predict.
Pick your ten most valuable buyer questions. Watch what Perplexity actually searches. Fix the buried themes first. That’s a week of work with more upside than a quarter of chasing head terms.