What is Query Fan-Out? How AI Splits One Prompt into Dozens of Searches

Query fan-out is the technique AI search engines use to break one prompt into 8 to 12 parallel sub-queries behind the scenes, retrieve passages for each, and merge the results into a single cited answer. Google AI Mode averages 10.7 sub-queries per prompt, ChatGPT generates 2 to 4, and Perplexity uses a single query 70% of the time. This guide explains what fan-out is, how it works across platforms, the latest research on its impact on citations, and what it means for businesses trying to earn AI visibility.

What Is Query Fan-Out and Why Does AI Search Use It?

AI search engines do not run one search per prompt. They decompose the prompt into several sub-queries, run them in parallel, retrieve passages for each, and fuse them into a single synthesized answer. Google patent language calls this query variant generation.

Traditional search matches your words against an index and returns a list. AI search works differently. It reads the intent behind a prompt, works out which separate questions need answering, and issues a batch of searches at once. If you want the wider system this sits inside, our AI SEO 2026 guide covers the full landscape. Fan-out is one mechanism within it.

Take a prompt like “best physiotherapist for running injuries in Toronto.” That sentence hides at least six questions: which clinics treat running injuries, which are in Toronto, what patients say, what credentials the practitioners hold, whether insurance covers it, and how long the wait is. No single search answers all of that. So the model splits the work into pieces it can answer, then reassembles them.

The rest of this article covers how many sub-queries each platform generates, what types they take, how results get merged, whether covering them earns citations, and what any of it changes for a local service business.

How Many Sub-Queries Does AI Actually Generate?

ChatGPT is far more restrained, averaging 2 to 4 searches per prompt after analyzing 8,500 prompts, and generating multiple queries 67.3% of the time, according to Qwairy’s 102,000-query dataset. Perplexity is the most economical of the three, sticking to a single query 70.5% of the time. Across platforms, AirOps found query expansion on 88.6% of queries in a dataset of 548,534 pages and 15,000 prompts. Engines differ enough that the same prompt can produce very different source lists. You need to know the cross-engine consensus gap in detail to figure out why this happens.

What Types of Sub-Queries Does AI Generate?

Google patent US11663201B2 documents eight synthetic query types: equivalent, follow-up, generalization, specification, canonicalization, language translation, entailment, and clarification. Each explores a different facet of the original prompt rather than repeating it.

Mike King’s reading of the patent at iPullRank sets out eight documented types. Here is each one applied to a single starting prompt: “best physiotherapist for running injuries in Toronto.”

• Equivalent. Alternative phrasing for the same intent. “Top physio running injury Toronto.”
• Specification. A narrower, more detailed version. “Shockwave therapy for runner’s knee Toronto.”
• Generalization. A broader version that widens the retrieval net. “Physiotherapy for sports injuries Ontario.”
• Follow-up. The logical next question a searcher would ask. “Do I need a referral for physiotherapy in Ontario?”
• Canonicalization. Standardized, textbook phrasing of a colloquial term. “Patellofemoral pain syndrome treatment.”
• Entailment. Something implied but never stated. A running injury query implies gait assessment and return-to-run programming.
• Clarification. A query that confirms ambiguous intent. “Physiotherapist vs sports medicine doctor for running injuries.”
• Language translation. The same question in another language, which matters in multilingual markets like Toronto and Montreal.

Practitioner taxonomies vary. Different analysts reading the same patent assign slightly different names to similar behaviors, and comparative queries (“physiotherapist vs chiropractor for running injuries”) are sometimes classified as clarification and sometimes as their own type. The labels matter less than the pattern: the engine is testing your topic from angles you did not write for. Information is the base, and that’s why original research wins AI citations.

How Fan-Out Results Get Merged: Reciprocal Rank Fusion

Reciprocal Rank Fusion merges the ranked result lists from every sub-query into one list. Each page scores according to its position in each list, so pages that appear consistently high across many lists rise, while a page that ranks in only one list sinks.

Once eight or ten sub-queries have each returned their own ranked list, the system has a merging problem. The standard solution is Reciprocal Rank Fusion, introduced by Cormack, Clarke and Büttcher in a 2009 SIGIR paper. Every document earns a score based on where it ranked in each list, and those scores are added up. A smoothing constant, conventionally set at 60, flattens the advantage of any single first-place finish so that one strong list cannot outvote broad agreement across many.

RRF is now the default hybrid merging method in Elasticsearch, OpenSearch, Azure AI Search, Weaviate and Qdrant. That matters for content strategy because of what the maths rewards. A page that ranks for one sub-query holds one ticket in the raffle. A page that ranks across six of them holds six. Mike King’s metaphor is the clearest way to hold it in your head, and it explains why topical depth beats single-keyword targeting under AI query decomposition.

Does Fan-Out Coverage Actually Improve AI Citations?

The evidence is genuinely split. Correlation studies find that pages ranking for sub-queries are far more likely to be cited. The largest controlled study finds that once retrieval rank is accounted for, sub-query coverage barely predicts citation at all.

What Fan-Out Means for Local Service Businesses?

Local prompts generate the fewest sub-queries of any vertical, averaging 3.79 against 11.7 for software. But web search fires on 59% of local-intent prompts in ChatGPT, the highest rate of any category. Fewer sub-queries, and each one carries more weight.

Most coverage of this topic is written for SaaS, which is why it stops being useful here. Nectiv’s vertical breakdown found “Local” is the lowest fan-out category at 3.79 average sub-queries, against 11.7 for software. The instinctive read is that fan-out matters less for a clinic or a contractor. The opposite is closer to true: ChatGPT triggers a web search on 59% of local-intent prompts, the highest rate of any vertical, so AI search sub-queries in local are fewer but more decisive. Missing one of four hurts more than missing one of twelve.

Look at what those lists point to. Only about half the assets are blog posts. The rest are reviews, Google Business Profile fields, and directory data. The three highest-value local surfaces are review content (recent, and naming the specific service), profile completeness (correct primary category, every service listed, Q&A populated), and consistent directory listings. Reviews naming a specific treatment or repair are worth more than their volume suggests: they are the only asset answering comparative and implied sub-queries in a voice the engine treats as independent.

None of that is content in the usual sense. It is structured business data, and it decays. A profile with the wrong category, or a service list untouched for two years, will fail sub-queries your blog posts answer perfectly well, because the engine never reaches the blog post. Half of a local fan-out audit is checking assets nobody thinks of as marketing.

The remaining question is whether the engine recognizes your business as a distinct entity at all. If the model cannot resolve your business name to a single confident entity, consistent data across every one of those surfaces is what fixes it. At Logik Digital, we audit fan-out coverage as part of every AI visibility engagement, mapping which sub-queries your assets already answer and which ones quietly hand the searcher to a competitor.

How to Audit Your Fan-Out Coverage?

Generate the sub-queries with a first-party tool, cluster them into themes rather than tracking them individually, gap-map those themes against pages you already own, then prioritize the missing themes most likely to change a buying decision.

1. Generate the sub-queries. Three practical options. The Gemini API with grounding enabled returns the exact queries in the web Search Queries field, which is first-party and the most accurate route for Google. ChatGPT’s sub-queries can be read in Chrome DevTools under the Network tab, in the conversations endpoint response, in the search_model_queries metadata. Qforia from iPullRank is free and simulates fan-out using a Gemini API key. The wider tooling landscape is reviewed in our best AI SEO tools guide.
2. Cluster by theme, not by query. With 95% of sub-queries carrying zero search volume and 66% appearing only once across repeated runs, individual queries are noise. Aggregate them into persistent themes: reviews, cost, credentials, insurance, availability, comparison. Themes repeat even when the wording does not.
3. Gap-map each theme against existing assets. For every theme, ask which page answers it. A service page, a blog post, a profile field, an FAQ? Mark the themes with no owner. This is usually where a business discovers three of its highest-value themes have no page at all. You can also track AI traffic in GA 4 that hides in the “Direct” section.
4. Prioritize by decisiveness. Not every gap is worth filling. Ask which missing theme would change whether someone chooses you over a competitor. Pricing transparency and availability usually beat a general explainer. Build those first.

One thing not to do: build thirty thin pages targeting individual sub-queries. That is the failure mode the Indig data argues against directly, and it produces exactly the kind of low-value page AI engines skip. Build topic clusters with passage-level depth instead, so the themes get covered naturally inside pages worth ranking on their own. Write structured content for AI search engines and follow the formatting side of that.

In the audits we run at Logik Digital, the pattern repeats: businesses are rarely missing content outright. They are missing the two or three decisive themes, usually pricing, availability, or proof, and they have those themes buried inside pages built for a different purpose. Finding them takes an afternoon. We do content pruning for AI and set out the full model for how your website will be structured and what the content format will be.

Where This Leaves You

The mechanism is real and well documented. The optimization industry built on top of it is mostly a relabeling of topic-cluster strategy, and the best available controlled evidence says chasing individual sub-queries will not move your citation rate. Your AI visibility changes month on month.

What does move it is ranking well, structuring passages so they can be extracted cleanly, and owning the themes that decide a purchase. Fan-out is the most useful diagnostic we have for finding which of those themes you are missing. Treat it as a map, not a target list. Want to know which AI sub-queries your business is missing? Connect with Logik Digital, and we’ll run AI visibility audits that map your fan-out coverage across every major platform.