What Is Query Fan-Out? How AI Search Splits Prompts (and How to Optimize for It)

What is query fan-out?

Query fan-out is the process an AI search system uses to answer a prompt by breaking it into multiple related searches, running them at the same time, and merging what it finds into one response. The individual searches are called fan-out queries or sub-queries. The user never types them and usually never sees them.

A classic search engine takes your query and returns a ranked list for that exact string. An answer engine does something closer to what a good research assistant does. It reads the question, works out every piece of information a complete answer would need, goes and looks up each piece, and then writes a summary with sources. Fan-out is the "go and look up each piece" step.

Google introduced the term publicly at Google I/O in May 2025 when it launched AI Mode. Head of Search Elizabeth Reid described it as the technique that lets Search break a question into subtopics and issue a multitude of queries simultaneously on the user's behalf. Google's own documentation for site owners now says both AI Overviews and AI Mode may use query fan-out, issuing multiple related searches across subtopics and data sources to develop a response.

The name stuck, and the industry now uses "query fan-out" for the equivalent behaviour in every retrieval-based assistant: ChatGPT search, Perplexity, Gemini, Copilot and Claude with web search. OpenAI, Perplexity and Anthropic don't use Google's term, but the mechanics are the same: rewrite, search several times, rank, synthesize.

Why AI search uses fan-out at all

Fan-out exists because prompts are longer and more specific than keywords. The average Google query is a few words. A prompt to ChatGPT is often a full sentence with constraints, context and a goal: "What's the best CRM for a 6-person agency that already uses Google Workspace and needs client portals, under $50 a seat?" No single page on the web answers that exact question. Plenty of pages answer parts of it.

Fan-out solves three problems at once:

• Coverage. Each constraint in the prompt (team size, integration, feature, budget) becomes its own search, so the answer can satisfy all of them instead of matching the most generic one.
• Grounding. Language models make things up when they answer from memory. Retrieving fresh documents for each sub-question gives the model evidence to quote and sources to cite, which lowers the hallucination rate.
• Freshness. A model's training data is months old. Sub-queries can add the current year, "latest" or "pricing" to pull recent pages, so the answer reflects prices and products as they are now.

The side effect for marketers is the important part: the set of pages an AI answer draws from is no longer the top ten results for one keyword. It is the union of the best results across every sub-query, re-ranked by how well each passage helps the final answer. That changes what it means to "rank".

How query fan-out works, step by step

The exact pipelines are proprietary, but Google's documentation and the visible behaviour of every major engine, point to the same five stages.

1. Understand. The model reads the prompt and identifies the intents inside it. "Best laptop for a college student who edits video and needs long battery life" contains at least three: suitable for students (price, weight), battery life, and video-editing performance. It also infers context the user didn't state, such as location from the account or the current date.
2. Decompose. Each intent becomes one or more search queries: "best laptops for college students 2026", "longest battery life laptops", "minimum specs for video editing laptop", "MacBook Air vs Dell XPS for video editing". Complex prompts produce more queries; simple factual prompts may produce one or none.
3. Retrieve. The system runs the sub-queries against one or more indexes: Google's web index, Bing (which several assistants rely on), its own crawl, shopping and local data, or a news feed. What it learns from early results can trigger a second round of more specific searches. This iterative loop is what "deep research" modes stretch out to minutes.
4. Score. Retrieved documents are split into passages and ranked for how well each passage answers each sub-query, and how much it adds to the overall answer. This is passage-level, not page-level: a page can lose on the head term and still contribute its best paragraph.
5. Synthesize. The model writes the answer from the top passages and attaches citations to the sources it used. Some sources are cited inline; others are used but only listed in a sources panel, and some influence the wording without being cited at all.

A worked example: one prompt, fourteen sub-queries

Here is a realistic decomposition of a B2B buying prompt, grouped by intent. It is the kind of output you get when you run the same prompt several times across engines and collect the distinct sub-queries.

Prompt: "What's the best project management tool for a 15-person marketing agency that bills clients hourly?"

Two things stand out. First, only one of the eight intents matches the head keyword ("best project management software for agencies"). Seven are attributes, comparisons, risks and next steps. Second, a single well-built page can answer five or six of them: a pricing table, a "who it's for" section, a time-tracking section, a client-portal FAQ and an honest limitations list. That page becomes a candidate for many sub-queries at once, which is exactly what the scoring stage rewards.

The 8 types of fan-out queries

Most guides list six types. In the sub-queries we capture from live answers, eight patterns recur often enough to plan content around. Knowing the type tells you what kind of content block satisfies it.

The two types most brands ignore are attribute and risk. Engines check specific properties before recommending something, and they actively search for drawbacks to give a balanced answer. If your site doesn't state your pricing, integrations or limitations plainly, the engine gets those facts from a review site, a Reddit thread or a competitor's comparison page, and you lose control of the framing.

How each AI engine fans out

Every assistant fans out, but not identically. The differences matter because they change which index your page has to be in and how many sub-queries a typical prompt generates.

The practical takeaway: being indexed by Google is necessary but not sufficient. ChatGPT and Copilot lean on other indexes and crawlers, so your pages also need to be crawlable by Bingbot, OAI-SearchBot and PerplexityBot. Our free AI crawler checker tells you in a few seconds whether your robots.txt blocks any of them. For engine-specific tactics, see our guides to ChatGPT, Perplexity, Gemini and Claude.

Fan-out queries vs long-tail keywords vs People Also Ask

Fan-out queries look like long-tail keywords, and they overlap with People Also Ask questions, which leads to a common mistake: treating them as a keyword list. They are different things with different uses.

The rule of thumb: keywords tell you what to call a page, fan-out tells you what the page must contain. A fan-out query with zero search volume can still be the sub-question that decides whether an engine cites you, because it is asked on behalf of every user who types the parent prompt.

Why query fan-out matters for AI visibility

Fan-out is the mechanism behind most of the things marketers notice about AI search and find confusing.

• It explains citation patterns. When a mid-ranking review site gets cited over the category leader, it is usually because that site answers more of the sub-queries in clean, extractable passages. Our explainer on LLM citations goes deeper on what gets a page cited.
• It explains volatility. Run the same prompt twice and you get different sub-queries, so different pages get retrieved, so different brands appear. Visibility in AI search is a rate across many runs, not a fixed position. That is why serious tracking samples each prompt repeatedly.
• It rewards depth over volume. Ten thin pages each targeting one keyword lose to one thorough page that answers a cluster of related sub-questions. Topic coverage is now measured at the passage level.
• It pulls in third-party sources. Risk and comparison sub-queries send engines to review sites, forums, directories and news. Your off-site footprint becomes part of the answer whether you manage it or not.
• It changes how you measure SEO. A page can win citations without ranking first for anything you track. If you only report rankings, you will miss both the wins and the losses. That is the case for tracking AI share of voice alongside rankings.

How to find fan-out queries (7 methods, free and paid)

Because fan-out is synthetic and probabilistic, no method gives you "the" list. The goal is to collect enough sub-queries from enough runs to see which intents keep coming back. Start with the free methods to learn the shape of your market, then automate.

1. Read the searches Claude runs

With web search on, Claude lists the searches it performs while answering. Ask your target prompt, expand the search steps, and copy every query. Re-run the prompt three to five times in fresh chats; the queries that repeat are your core intents.

2. Read Perplexity's steps panel

Perplexity shows its search steps for multi-step answers, particularly in Pro and Research modes. The steps read like a research plan. Note both the queries and the sources it opens for each, because the sources tell you who currently owns that sub-question.

3. Use Gemini Deep Research's plan

Gemini Deep Research shows a research plan before it starts. That plan is a fan-out in plain English: the list of sub-topics it intends to cover. It is the clearest view any engine gives of how a full answer is decomposed.

4. Ask the model to show its decomposition

Ask ChatGPT or Gemini: "Before answering, list the separate web searches you would run to answer this question completely, then answer it." This is not the engine's real fan-out, since the model is simulating, but it is a fast, free approximation that tends to surface the same intent categories.

5. Mine Google's own surfaces

People Also Ask boxes, AI Mode's suggested follow-ups and the "things to know" style sub-heads in AI Overviews all reflect how Google decomposes a topic. Search your prompt and its two or three shortest variants, then collect every question and sub-heading you see.

6. Pull fan-out from an API

Data providers such as DataForSEO return the search queries ChatGPT issued alongside its answer in their LLM response endpoints. This is real fan-out rather than a simulation, and it scales to hundreds of prompts. It needs a developer and an account with the provider.

7. Track it continuously in an AI visibility platform

A tracking platform runs your prompts on a schedule, stores the sub-queries and cited sources from each run, and shows how they change. In Measure, the Live scan fans one of your prompts into the ways real buyers phrase it, runs each phrasing across the engines you pick and aggregates a mention rate; scheduled tracking stores the fan-out queries ChatGPT returns with each answer. Most dedicated tools in our AI search visibility tools roundup now expose some version of this.

How to decide which sub-queries to cover

A single market can produce hundreds of distinct sub-queries. You can't write for all of them, and you shouldn't. Score each intent cluster on four factors from 1 to 3 and work from the top down.

In practice the first wave is almost always the same for every brand: attribute sub-queries about your own product (pricing, integrations, limits, setup time), comparative sub-queries against your top three competitors, and risk sub-queries about your category. These have high buying weight, you have the most authority on them, and most sites cover them badly.

How to optimize for query fan-out: an 8-step playbook

1. Start from prompts, not keywords

Write down the 20 to 50 questions your buyers actually ask an assistant at each stage: discovery ("how do I solve X"), shortlisting ("best X for Y"), comparison ("A vs B") and validation ("is A worth it"). Use sales call notes, support tickets and onboarding surveys, not just keyword tools. These prompts are what you will fan out and track. Our guide to generative engine optimization covers how to build a prompt set.

2. Collect and cluster the sub-queries

Run each prompt through the discovery methods above and pool the results. Cluster sub-queries by intent, not wording: "[tool] price per seat", "how much does [tool] cost" and "[tool] pricing 2026" are one intent. Tag each cluster with its type from the eight above. Then score and rank the clusters.

3. Map each intent to a page section

Build a coverage matrix: intents down the side, your existing pages across the top, and a mark for strong, partial or no coverage. This is the single most useful artifact in fan-out work, because it shows at a glance which intents have no home on your site.

For each gap, decide whether it belongs as a new section on an existing page or as a new page. A useful test: if the intent needs more than about 300 words and has its own comparison or how-to shape, give it a page. If it is a fact or a short answer, make it a section or an FAQ on the closest page.

4. Build pillar and cluster pages around the topic

Group related intents into a topic cluster: one pillar page that answers the broad prompt and links out, and cluster pages that each own a group of sub-queries in depth. Link them both ways with descriptive anchors. Clusters help retrieval in two ways: more of your pages are candidates for more sub-queries, and the internal links help engines understand which page is authoritative for which sub-topic.

5. Write in self-contained, answer-first chunks

Scoring happens at the passage level, so every section should make sense if it were the only thing the engine read. Concretely:

• Put the answer in the first sentence under each heading. "[Tool] costs $12 per user per month on the Team plan, billed annually." Then explain.
• Phrase headings like sub-queries. "Does [tool] have a client portal?" beats "Collaboration features".
• Restate the subject. Write "[Tool]'s free plan includes..." instead of "It includes...". A passage lifted out of context loses the pronoun's referent.
• Define terms where you introduce them, because definitional sub-queries ("what is X") are common and easy to win.
• Use tables for attributes and comparisons. Engines extract structured rows cleanly and often reproduce them.
• Keep paragraphs short and specific. Numbers, names, dates and limits are what get quoted. Adjectives don't.

6. Answer the attribute and risk sub-queries on your own site

This is the highest-return step for most brands. Publish the facts engines go looking for, on pages you control:

• A pricing page with real numbers, what each plan includes, and the date it was last updated.
• An integrations page that lists each integration by name, not a logo wall.
• A plain "who it's for and who it's not for" section on product pages.
• An honest limitations or FAQ section that answers the objections buyers raise.
• Security, compliance, data location and support hours stated in text, not only in a PDF.
• Comparison pages against your main competitors that are fair enough to be credible.

If you don't answer "[your brand] drawbacks", someone else will, and the engine will quote them.

7. Make your pages easy to retrieve and parse

• Allow the AI crawlers (OAI-SearchBot, PerplexityBot, Claude-SearchBot, Bingbot, Googlebot) in robots.txt and make sure your CDN or firewall doesn't block them.
• Render key content in HTML. Several AI crawlers don't run JavaScript, so content that only appears after client-side rendering may be invisible to them.
• Add schema markup (Product, Offer, FAQPage, HowTo, Organization, Article) so facts like price and availability are machine-readable. Schema doesn't earn citations on its own, but it reduces ambiguity when a sub-query asks for a specific attribute.
• Show freshness. Visible "last updated" dates and current-year facts help with recency sub-queries. Update the content, not just the date.
• Consider an llms.txt file that points assistants to your most important pages. It is not a ranking factor, but it is cheap.

8. Cover the sub-queries that happen off your site

Comparison, risk and "best X" sub-queries routinely retrieve third-party pages. You can't write those pages, but you can influence them:

• Find which domains get cited for your prompts (a citation source report does this) and prioritize being listed, reviewed or mentioned there.
• Keep directory and review profiles (G2, Capterra, Trustpilot, Google Business Profile) complete and current.
• Answer real questions in the communities engines cite for your category, such as specific subreddits or forums.
• Publish original data that journalists and roundup writers will reference, which earns mentions in the pages engines retrieve.
• Correct inaccurate facts about your brand on third-party pages when you find them.

Page patterns that win multiple sub-queries

Some page formats naturally answer many sub-queries at once. If you are deciding what to build first, these five have the best ratio of effort to fan-out coverage.

How to measure fan-out coverage

Optimizing for fan-out without measuring it is guesswork. Track three layers, from what you control to what you want.

1. Fan-out coverage rate (input)

For each tracked prompt, the share of its recurring sub-query intents that your site answers clearly. If a prompt has ten recurring intents and you have a strong section for six, coverage is 60%. Recalculate it each time you refresh your sub-query list. This is the number your content team can move directly.

2. Mention and citation rate (output)

The share of runs in which an engine mentions your brand, and the share in which it cites your domain, per prompt and per engine. Because fan-out makes answers vary, these have to be measured across repeated runs. See our explainer on AI share of voice for how to compare yourself with competitors on the same prompts.

3. AI referral traffic and conversions (outcome)

Sessions and conversions from chatgpt.com, perplexity.ai, gemini.google.com, claude.ai and copilot in GA4, plus AI feature impressions in Search Console's generative AI report. This connects the content work to revenue. Our guide to measuring AI search ROI covers the setup.

Common query fan-out mistakes

• Treating sub-queries as keywords. Writing a separate thin page for every fan-out phrasing creates near-duplicate content and dilutes authority. Cover intents, not strings.
• Trusting a single run. Fan-out changes between runs, engines and users. Decisions based on one screenshot are noise.
• Stuffing every sub-query into one page. A 10,000-word page that answers everything loosely loses to focused pages that answer each intent well. Use clusters.
• Hiding facts. "Contact us for pricing", image-only spec sheets and PDF-only security docs leave attribute sub-queries to third parties.
• Ignoring the off-site half. Comparison and risk sub-queries often retrieve review sites and forums. Perfect on-site coverage can't fix a bad G2 profile.
• Blocking the crawlers. A robots.txt rule or bot-protection setting that blocks OAI-SearchBot or PerplexityBot removes you from those engines' retrieval no matter how good the content is.
• Measuring only rankings. Fan-out decouples citations from head-term rankings. Without prompt-level tracking you can't see the effect of the work.

A 30/60/90-day query fan-out plan

The bottom line

Query fan-out turns one prompt into many searches, and the brands that get cited are the ones whose pages answer the most of those searches clearly. You can't see every sub-query and you can't target them one by one, but you don't need to. Collect them across several runs, find the intents that keep recurring, give each intent a clear, self-contained answer on a page you control, make sure AI crawlers can reach it, and measure whether engines start citing you.

If you want to see where you stand first, run your brand through our free AI visibility checker. It takes a minute and needs no card.