Query Fan-Out Explained: What It Is and How to Optimize for It in 2027

Query fan-out is what happens when an AI system turns one question into several related searches before it answers. Google defines it as a set of concurrent, related queries that the model generates to pull in additional search results for the user’s question.

For generative engine optimization (GEO), the practical question is which sub-questions an AI asks about your topic and whether your content answers them. The sections below explain how fan-out works in Google AI Mode, ChatGPT, Perplexity and Claude, how ChatGPT’s behavior changed during 2026, and how to adjust your pages: which sub-questions to cover, how to write each section so it works on its own, and where independent evidence strengthens a claim.

What Exactly Is Query Fan-Out?

Query fan-out is the process where an AI or search system takes one user question and splits it into several sub-queries, so it can explore a wider or deeper set of possible answers. Each extra search an AI runs for one question is a fan-out query. Some sources call them sub-queries.

Take the question “best protein for runners.” The system might fan out into searches such as:

• “best whey protein”
• “plant-based protein”
• “post-run recovery supplements.”

Each fan-out query covers one subtopic of the original question. The question itself stays the same, and the system runs the extra searches it needs to answer it fully. A page that answers only the main question can miss the pages the AI pulls in (retrieves) for the subtopics.

Google named the technique publicly when it launched AI Mode, where one prompt can trigger several searches at once. Other AI answer engines, including ChatGPT, Perplexity and Claude, use the same technique in their own ways, as the next sections show.

Fan-out happens in a minority of ChatGPT responses, and the count depends on the model, the plan and how the data was collected. Here is what studies have measured, by ChatGPT model:

ChatGPT model	Source and setup	Searches run
GPT-5.3 Instant	Resoneo, analysis published May 2026	Typically 2 or 3 search rounds per response
GPT-5.4 Thinking	Resoneo, paid reasoning model	Often more than 10 per response
Model not specified, April 2026	Peec (an AI visibility tracker), 5M fan-outs across engines	2.1 per prompt
GPT-5.6 Luna, Aug to Sep 2026	Ahrefs, free and logged-out, 66.6M responses	14.4% of responses (about 1 in 7) had fan-outs, about 1.6 each
GPT-5.6 Sol	Nectiv, API, about 4,000 prompts	7.61 per prompt
GPT-6, early data, Oct 2026	Peec, 5.7M fan-outs from 2.2M responses	2 per chat, down from 3 on GPT-5.6

Read these as snapshots. Free logged-out data, paid reasoning models and API collection give very different counts, so it makes sense to compare figures only within the same setup.

How Query Fan-Out Works Across AI Engines

Most AI search engines handle a question in roughly the same four steps, and each step is a place where your content can be missed:

• Decide whether to search. Some questions are answered from what the model already knows, with no search at all.
• Fan out. The engine turns the question into several searches and runs them at the same time.
• Retrieve. It reads the pages that come back.
• Answer and cite. It writes one answer and links to some of its sources. A citation is a linked source shown with the answer.

A page can be skipped because the engine never searched, because none of the searches matched it, or because it was retrieved but never cited. In AirOps’ March 2026 study, ChatGPT left 85% of the pages it retrieved uncited. The engines differ in how they handle each step.

Google AI Mode: One Question, Many Subtopics

Google describes fan-out as a set of concurrent, related queries generated by the model to fetch more search results. Take “best protein for runners.” AI Mode can search for whey protein, plant-based protein and post-run recovery at the same time, then combine the results into one overview with cited sources. Content that answers only “best protein for runners” can miss the pages retrieved for each subtopic.

ChatGPT: Fewer Searches by Default, Sometimes Aimed at Sites It Knows

ChatGPT fans out in a minority of responses. Ahrefs found fan-outs in 14.4% of free, logged-out responses, while Resoneo saw more than 10 per response on a paid reasoning model. ChatGPT also sometimes uses the site: operator, which limits a search to one website. Ahrefs found it in 6.8% of fan-outs, but studies of commercial prompts found far more (23% to 64%).

Which websites? In Ahrefs’ data, site: searches spread across about 116,000 domains. Among the roughly 160 most-searched sites, three in four were brand sites such as apple.com, ikea.com and nike.com. The rest were mostly retailers, government sites such as gov.uk and gov.br, and Reddit, the ninth most site:-searched domain in the dataset. Lily Ray’s observations point the same way: ChatGPT goes to a brand’s own domain for specs and pricing, to .gov domains for health and legal questions, and to Reddit and review platforms for opinions.

That matters for brand building, because ChatGPT has to name a domain before it can search it. Resoneo argues that the model builds its queries around sources it already knows, so a brand it has never encountered may not be considered at all, and Lily Ray advises building a brand that ChatGPT thinks of first. The data fits that reading, since the most-searched domains are large consumer brands. Ahrefs’ own summary is that there are two ways into ChatGPT’s answers: be well enough known to appear in them, or be the original source ChatGPT finds when it searches.

For your own site, that means putting prices, specs and support details in plain, crawlable text, because a search scoped to your domain can only find what it can read. For sites you do not own, it means building a real presence where ChatGPT looks for opinions. Reddit is the clearest case: ChatGPT cites it far less than before, yet keeps searching it. A source can shape an answer without being credited. Our guide to using Reddit in your AI search strategy shows how to audit subreddits and take part without spamming.

Perplexity: Fewer, Simpler Searches

In Peec’s April 2026 data, Perplexity averaged 1.4 fan-outs per prompt against 2.1 for ChatGPT, and mostly stripped filler words and re-ran a cleaner version of the same question. The wording of the original query is the main thing to match.

Claude: Often No Search at All

In July 2026 tests by Profound, an AI visibility platform, Claude used web search for 36.6% of prompts. Words such as “best” and “near me” and a current year tended to trigger a search, while basic “what is” and “how does” questions usually stayed inside the model. For those questions, what the model already knows about your brand counts for more than your rankings.

When Claude does search, its fan-outs are predictable. It added a year to 94% of them, against 17% for ChatGPT, and the same query strings appeared in about 65% of repeated runs. Profound found that adding the year to a title made it 17% more similar to the queries Claude generated, so use a current year where the answer changes over time and update the evidence behind it. Claude’s cited URLs also overlapped heavily (79.2%) with the top 10 results of Brave, a search engine with its own index, so check your Brave rankings. Profound calls that overlap observational and not proof of cause.

Why LLMs Use Query Fan-Out in the First Place

Fan-out gives an AI system more evidence to work with before it writes an answer. Three purposes follow from that.

Resolve Ambiguity

A query like “jaguar speed” could mean a car spec or an animal fact. Fan-out queries can test each reading, and the answer can lead with the likelier one.

Ground the Answer

A model that answers from its training alone can state wrong details with confidence. Grounding pulls in live pages instead. Google says it uses retrieval to improve the accuracy and freshness of AI responses, and links to pages that support what the response says. Fan-out gives that retrieval more angles to cover.

Diversify Perspectives

Different content types serve different needs: a clinical study, a buyer’s guide, a forum thread, a brand website. Fan-out lets the system draw on several of these formats within one answer.

How ChatGPT’s Fan-Outs Shifted Across 2026

ChatGPT’s default model changed in August, and again on October 7 when GPT-6 replaced GPT-5.6 in ChatGPT chat (Sol on paid plans, Luna on Free and Go from October 8). Fan-out behavior moved with each change. Read every figure below as a snapshot of one model, plan and period.

The Free-Plan Baseline

Let’s start with the free plan, where more than 90% of weekly users are, according to Resoneo. Ahrefs’ free, logged-out sample from August and September 2026, when GPT-5.6 was the default, is the baseline for the rest of this section.

GPT-5.6 Brought More Retrieval Rounds

GPT-5.6 searched more than GPT-5.5 did. Peec’s analysis by David Konitzny, published August 10 with no sample size stated, found that the share of prompts needing only one round of fan-outs fell from 94.0% to 43.5%. Multi-round retrieval became common, with second rounds appearing in 33.5% of prompts. Retrieved sources per prompt doubled from about 12 to 24, and site: use rose from 0.3% to 23% of fan-outs.

Fan-Outs Lean Toward Official Sources

Fan-outs also leaned toward official sources. In the same Peec comparison, the share of fan-outs containing “official” rose from about 5% to 9%, and Ahrefs saw it rise from 10.1% to 13.1% of English-language fan-outs between August and September.

GPT-6 Changed the Picture Again

GPT-6 shifted the picture again. In Peec’s early comparison of GPT-5.6 and GPT-6 (5.7M fan-outs from 2.2M responses, published October 9), fan-outs per chat fell from 3 to 2 and site: use dropped from 22.6% to 8.3%. “Official” doubled from 12.3% to 24.6%, comparison terms (“compare” or “comparison”) rose from 1.9% to 5.7%, and the average fan-out grew from 9 to 11 words.

Why the Site Operator Numbers Differ

Studies of GPT-5.6 disagree on how often ChatGPT limits a search to one site: 23% of fan-outs in Peec’s August data on GPT-5.6 Luna, 6.8% in Ahrefs’ August and September data, and 64% of queries in Nectiv’s API-based study. Ahrefs puts much of the gap down to prompt choice: its prompts reflect broad real-world search demand, while many other studies used commercial “best X” prompts that trigger brand lookups. The 8.3% in Peec’s GPT-6 data belongs to a different model.

Listicles: Share Versus Volume

Listicles (the ranked “best of” pages) show how page types gain and lose ground as ChatGPT’s models change, and why the percentages need care. A page type’s share of retrieved sources can fall for two reasons: the total pool of sources grew, or ChatGPT retrieves fewer pages of that type. Peec’s tracking of ChatGPT’s retrieved sources from July to September separates the two. Peec set July to 100 for each measure, so later numbers show change relative to July: total retrieved sources rose to 175 while listicles stayed near 92, so listicles lost share through dilution. Peec’s early GPT-6 data points the other way, with the listicle share of cited URLs rising from 6.45% to 9.70%.

Reddit: Cited Less, Consulted Often

Reddit is another example of how fast a source can swing as ChatGPT changes. In Peec’s tracking, Reddit fell to 31 in retrieved sources, a real drop, after a May spike caused by ChatGPT appending “reddit” to fan-out queries between May 4 and 25. Citations fell too: Ahrefs measured an 88% drop in ChatGPT’s Reddit citations between March and September 2026.

Reddit remains in heavy use anyway: Ahrefs ranks it the ninth most site:-searched domain. These datasets differ in method and timing, so read them as signs that ChatGPT keeps using Reddit even as it cites it less.

More Source Warnings in GPT-6 Answers

GPT-6 also brought more source warnings. In a Peec sample of nearly 3M chats that cited a listicle or comparison page (October 7 to 12), notes saying a claim was “not independently verified” rose from 0.56% to 3.46% of chats within two days of GPT-6 becoming the default, and notes about vendor or marketing claims rose from 1.01% to 2.27%. Warnings appear in a minority of chats, and listicles continue to earn citations. A citation does not mean ChatGPT trusts the claim, so back yours with independent reviews, third-party benchmarks and verifiable data instead of relying on self-published rankings. For PR and communications teams, this raises the value of third-party coverage and independent reviews. They come from outside the brand, so they can back up a claim in a way the brand’s own pages cannot.

What These Studies Tell Us

Three takeaways from the studies above:

• Each model update changes how ChatGPT behaves.
• ChatGPT leans toward official, first-party sources.
• More pages are retrieved while fewer are credited.

Two habits follow. Before changing your GEO strategy, check absolute volumes of citations next to percentages. And re-test after every model update, because none of these figures carries over to the next model.

The SEO Impact

The model-by-model changes above lead to one practical point for SEO. Whatever the model, the searches that decide which pages an AI engine retrieves are written by the AI, and nobody typed them. Take “best CRM software for small businesses.” An AI tool might fan that out into “best free CRM tools,” “CRM with email automation” or “HubSpot vs Zoho for small teams.” Each of those searches can surface different brands and different citations, and a page built around one primary keyword competes for only one of them.

AirOps’ March 2026 study of ChatGPT (15,000 prompts) shows the size of the gap:

• 95% of ChatGPT’s fan-out queries had zero monthly search volume by traditional metrics.
• 32.9% of cited pages that appeared in a Google top-20 result surfaced only for a fan-out query, not for the starting prompt.

The same study found that 55.8% of cited pages ranked in Google’s top 20 for at least one query, original or fan-out, so ranking work carries over into citations.

Keyword volume tools miss fan-outs, because the AI generates those searches. Fan-out tools such as Peec, Profound and Searchable, fill that gap, and the next section shows how.

Google’s guidance also notes that its systems can match a page to a query without an exact wording match, so capturing every variation is unnecessary.

Optimizing for Query Fan-Out

Keyword tools miss fan-outs, and chasing every variation with a new page is unnecessary, so optimizing for query fan-out calls for a different workflow. You decide who you are trying to reach, find the questions AI asks on their behalf, and build pages that answer those questions well. The steps below follow that order.

1. Define Who You Are Optimizing For and What They Ask

Before you expand any prompt, you need the right prompts to expand. Fan-out analysis only helps when the starting prompt is something your ideal customers really ask an AI, so the first job is to decide which prompts are worth optimizing for.

Begin with your ideal customer profile: who buys, what problem they are solving, where they are in the buying process and the words they use to describe it. Then write the core prompts that person would put to an AI engine, from early questions (“what is…”, “how do I…”) to comparison and buying questions (“best X for Y”, “X vs Z”). Sales and support teams’ conversations, your website’s Search Console (it includes prompts typed into AI Mode) and forum discussions in your industry show the words your customers use, so these are a good place to start. It’s also a good idea to verify whether there is any real search volume behind these queries using tools like Ahrefs or Semrush. Check this for the core prompts, not for their fan-outs, which rarely have any search volume.

Prompt sets generated automatically by a tracking tool or a chatbot, without this groundwork, can lean on brand searches and miss the questions people really type. The starting prompt shapes everything built on it, so a weak one produces a weak fan-out analysis.

Once the core prompts are agreed, the next step is to expand each one into its variations and the sub-queries an AI is likely to run.

2. Fan Out Each Prompt

Take each core prompt and expand it the way an AI would, into its variations, related meanings and sub-topics. Combine several sources:

• SEO tools (Ahrefs, Semrush, AlsoAsked) and Google’s People Also Ask, to map common follow-up questions.
• Prompts such as “Expand this query into related search intents” in ChatGPT or Claude, to see how an AI might read the topic.
• Tools that show real ChatGPT fan-outs for the prompts you track: Peec AI, Profound, Searchable and Ahrefs Brand Radar (ChatGPT and Perplexity). To read the queries behind a single answer, you can use the free Resoneo plugin for Chrome, which shows the searches ChatGPT runs and the sources it pulls.
• Your site search bar or chatbot logs, plus the questions your support and sales teams hear.
• Forums such as Quora and Reddit, for the exact language people use to describe their problems. Our Reddit guide walks through auditing subreddits for pain points and the phrases people use for them.

Fan-out tools show which searches AI runs. A simpler check comes first: does your brand appear in AI answers at all? ChatGPT can only limit a search to a site it already knows, so brand presence comes before any fan-out work.

3. Turning Fan-Out Insights into Optimized Content

Once the query clusters are mapped, build content that covers them in depth.

Focus on entities, not just keywords

Entities are the people, brands, products and concepts a page is about. Make sure your content clearly references the related entities that appear across the fan-out branches.

Build semantic completeness

Cover the sub-questions as sections or FAQ blocks on one page, and create a separate page only when it serves a distinct reader need. Writing content for every query variation or fan-out query, mainly to influence rankings or AI responses, violates Google’s scaled content abuse policy, as its AI optimization guide spells out.

For marketing teams, that means briefing each sub-question as a section in the content brief, so writers cover the fan-out branches from the start.

Write sections that stand on their own

Some advice says to write in short, self-contained chunks because AI reads pages in pieces. AI retrieval systems do split pages into blocks, also called chunks, and score each block against the search. The data does not support writing to a block size, though. On October 21, Peec’s David Konitzny examined how ChatGPT splits and scores a page. The block boundaries did not follow paragraphs, cards or sections, one block started halfway through an FAQ answer, and large, information-dense blocks could score highly.

What mattered more was how closely a block matched the query. Blocks covering more than 50% of the query terms were almost three times as likely to score above 0.99 as blocks covering less than 25% (61% against 23%). Blocks at the top of a page tend to score higher, but when two blocks cover the same share of terms, the top one scores no better. Titles and introductions do well because they summarize the page and so cover more terms. Google also says there is no need to break content into tiny pieces.

In practice, write each section so it makes sense on its own: name the subject, state the answer early and cover the terms a searcher would use. Our guide to writing for robots covers sentence-level habits.

Strengthen connections

Link each page to the pages that cover its related sub-questions, using descriptive anchor text, so readers and crawlers can see how the topic fits together.

⟶ Internal Linking: Everything You Need to Know: Query Fan-Out Explained: What It Is and How to Optimize for It in 2027

Track by model and date

Fan-out behavior changes with each model update, so a one-off test goes out of date quickly. Re-test a fixed set of prompts after every change to ChatGPT’s default model, and record the model, plan, date, fan-out queries and cited sources so you can compare runs.

Winning When Queries Multiply

When you review or optimize a page for a query, one test is worth running: what does this page say that no other page does? Google’s guidance points the same way. It says unique, non-commodity content is likely to influence visibility in generative AI search, in the long run, more than other optimization tactics.

That matters even more when AI engines turn one question into many. With AI tools, anyone can produce an article for every query and sub-query in minutes, so there is no shortage of content on any topic. Writing one more page per query gets you nowhere. Google’s guidance tells publishers not to recycle what others have already said or what a generative AI model could easily produce. What sets a brand apart is original, in-depth advice on the topic.

When we talk to brands, we usually find they already have that advice. They are experts in what they do, with a point of view, experience and knowledge that no generic page can copy. That is the value their target audience is looking for, and sharing it consistently positions them as the go-to solution for their potential customers.

A good GEO or SEO partner helps that expertise reach the right people. They work with the business to find out what it has to share, work out how it breaks down into queries and sub-queries, decide which ones have the potential to bring the most impact, and write the content, structured so that both readers and AI engines can use it.

Whether you are an agency partner or a brand, you do not need to master how query fan-out works or how it shifts from one model to the next. Keep doing what you do best. With SUSO at your side, you have someone who is making sense of it all and applying it to your strategy, and that is the work we take on.

FAQs

• How does query fan-out work in ChatGPT, Google AI Mode, Perplexity and Claude?

  Google AI Mode splits a question into subtopics and runs many searches at once. ChatGPT fans out in a minority of responses (14.4% of free responses in Ahrefs’ data) and sometimes limits its searches to specific sites, more often on commercial prompts, while paid reasoning models run more fan-outs. Perplexity averaged 1.4 fan-outs per prompt in April 2026, mostly cleaned-up versions of the original question, and Claude searched for 36.6% of prompts in July 2026 tests and answered the rest from what the model already knows. It’s worth re-testing each engine after model updates.

• Should I create a page for every fan-out query?

  No. Cover the sub-questions as sections of one page and add a new page only for a distinct reader need. Google says writing for every variation, mainly to influence AI responses, breaches its scaled content abuse policy, and Lily Ray adds that fan-outs are long-tail and vary between prompts and users. Instead, look at the fan-out queries the model runs again and again for your topic, and group them into a few core topics to cover well.

• How can I find the fan-out queries for my topic?

  Use a tool that shows ChatGPT’s fan-outs for the prompts you track, such as Peec AI, Profound or Searchable, or the free Resoneo plugin to read the queries behind a single answer. Search Console cannot show them, because it logs the prompts people type into AI Mode and not the sub-queries Google runs behind them. Keyword tools are not an ideal source of fan-outs, because 95% of ChatGPT’s fan-out queries had zero search volume in AirOps’ data.

• Which prompts should I track and optimize for?

  The prompts your ideal customers actually ask. Good sources include sales and support questions, site search and chatbot logs, long question-style queries in Search Console and forums. Prompt sets generated automatically by a tracking tool or a chatbot can lean on brand searches and miss the questions people really type, which skews any fan-out analysis built on them.