What Is Query Fan-Out? How to Find & Optimize for Sub-Queries (2026 Guide)

When someone asks Google AI Mode or ChatGPT a real question, the AI doesn’t run one search. It quietly runs many: comparisons, price checks, “best of 2026” lists, local variations, follow-up steps. Then it stitches the best passages into one answer and cites a handful of pages.

That behind-the-scenes process is called query fan-out, and it’s the reason a page can rank #1 on Google and still never appear in an AI answer, while a page on page two gets cited.

This guide explains exactly what query fan-out is (using Google’s own words), how it works across Google, ChatGPT, Perplexity and Grok, what the latest research shows, and a practical 5-step workflow to find the sub-queries behind your buyers’ prompts and make your content the one AI engines pick.

What Is Query Fan-Out?

Query fan-out is the step where an AI search engine turns your single question into a set of smaller searches. Google’s own documentation defines it as “a set of concurrent, related queries generated by the model to request more information and fetch additional relevant search results to address the user’s query.”

Google’s example is simple: for the prompt “how to fix a lawn that’s full of weeds”, fan-out queries might include “best herbicides for lawns”, “remove weeds without chemicals” and “how to prevent weeds in lawn”. Each of those is a separate search, and each can surface a different page.

You’ll also see the same idea called query decomposition, sub-queries or synthetic queries. The names differ, but the mechanism is the same: the AI searches on your behalf, from several angles, before it writes a word.

Where the Term Came From

Google introduced “query fan-out” publicly at Google I/O in May 2025, when it launched AI Mode. In the I/O announcement, Google’s Head of Search, Elizabeth Reid, explained that AI Mode breaks a question into subtopics and issues “a multitude of queries simultaneously” on the user’s behalf. The same post described Deep Search, which takes the technique further and “can issue hundreds of searches” to build a fully cited report.

Since then, Google has made fan-out more aggressive, not less. When Gemini 3 arrived in Search in November 2025, Google said AI Mode can now “perform even more searches” and find content it “may have previously missed”. And in a March 2026 interview, Google’s Search Senior Engineering Director Dounia Berrada summed it up: “AI Mode is basically doing a dozen searches for you in the time it takes to do one.”

Importantly, it isn’t just AI Mode. Google’s AI features documentation states that both AI Overviews and AI Mode may use query fan-out, which is why AI answers often link to a wider and more diverse set of pages than the classic ten blue links.

How Query Fan-Out Works: 5 Stages

Every platform implements it differently, but the pipeline looks broadly the same:

1. Analyze. The model interprets the prompt: what the person actually wants, what context they gave (budget, location, team size) and what they didn’t say but probably need.
2. Decompose. It writes a set of sub-queries, each covering one angle of the question.
3. Retrieve. It runs those searches in parallel against a search index (Google’s own index for AI Mode, a web search partner for ChatGPT) and pulls candidate pages.
4. Score. It evaluates passages from those pages for relevance, specificity and trust, and picks the best evidence for each angle.
5. Synthesize. It writes one answer and attaches citations to the pages it relied on.

Here’s the part most people miss: you don’t compete for the original prompt. You compete for each sub-query. If your page is the best answer to “HubSpot vs Zoho CRM for startups” but says nothing about pricing in INR, you may win one citation and lose the rest of the answer to competitors who covered the other angles.

Query Fan-Out Across AI Search Platforms

The technique is common to most AI search products, but how much they fan out, and what they add to your prompt, varies a lot.

6 Types of Fan-Out Queries

Sub-queries aren’t random. They follow recognizable patterns, and once you can spot them, you can plan content for them. These six categories, adapted from Semrush’s fan-out query taxonomy, cover most of what AI engines generate:

• Reformulation: the same intent in different words (“set up Google Business Profile” becomes “create a Google Business listing”).
• Comparative: weighing options (“electric vs manual standing desk”).
• Recency: adding the current year or “latest” (“GST rules for SaaS 2026”).
• Implicit: filling a need the user didn’t state (“attractions with lift access” for a wheelchair-friendly query).
• Contextual: adding location, budget or user context (“gyms with childcare in Indiranagar”).
• Next step: anticipating the follow-up action (“trademark lawyer near me” after “how to register a trademark”).

Why Query Fan-Out Matters for SEO and AI Visibility

Three findings from 2025–2026 research explain why fan-out has become the lever that decides who gets cited:

• Fan-out is growing fast. Nectiv Digital re-ran about 4,000 prompts through ChatGPT’s API in August 2026 and found an average of 7.61 fan-out queries per prompt, up from 2.17 a year earlier, with up to 29 searches for a single prompt.
• Platforms fan out very differently. Peec AI analyzed 5 million fan-outs in April 2026 and measured averages of 1.4 per query on Perplexity, 2.1 on ChatGPT and 6.8 on Grok. It also found ChatGPT injecting words like “best” (15.3% of affected queries), “reviews” (6.8%) and the current year (5.4%) that users never typed.
• Ranking for sub-queries predicts citations. Surfer’s study of 173,902 URLs across 10,000 keywords found that pages ranking for fan-out queries were 161% more likely to be cited in AI Overviews than pages ranking only for the main query, and 68% of cited pages sat outside the organic top 10.

The takeaway: traditional rank tracking tells you whether you win the head keyword. It doesn’t tell you whether you win the ten searches the AI ran behind it. That gap is where brands disappear from AI answers, and where smaller, sharper sites overtake bigger ones (we break down how in GEO for startups).

Query Fan-Out vs Long-Tail Keywords vs Query Decomposition

These terms get mixed up, and the difference changes how you should act:

Query decomposition is simply the computer-science name for the “decompose” stage: splitting a complex question into smaller ones. Query fan-out is the full loop of decomposing, searching in parallel and merging the results.

How to Find Fan-Out Queries

Because fan-out queries are generated on the fly, you can’t pull them from a keyword database. You have to observe them. Here are four methods, from most to least direct.

1. Capture ChatGPT’s sub-queries with a browser extension

ChatGPT is the easiest platform to observe, because its web searches can be captured in the browser. A fan-out capture extension for Chrome, such as PromptPulse, the free Chrome extension we built at AI SEO Solutions, shows you every search query ChatGPT runs behind a prompt, alongside the sources it retrieved and cited, so you can see exactly which angles the model explored and which pages won each one.

How to use it:

1. Install a fan-out capture extension in Chrome and open ChatGPT.
2. Ask a prompt your buyers would really ask, for example “best AI SEO agency in Bangalore for a SaaS startup”.
3. Open the extension panel to see the sub-queries, cited URLs and brands mentioned.
4. Run the same prompt 3–5 times and export the results. One run is a sample; several runs show the pattern.

Tool We Built

PromptPulse: LLM Visibility Tracker is the free extension referenced above — it records every ChatGPT search query, brand and competitor mention, and cited link locally on your device (no cloud uploads), then exports to CSV. Get PromptPulse free on the Chrome Web Store →

2. Reverse-engineer Google AI Mode from its sources

Google doesn’t expose AI Mode’s sub-queries, but you can infer them. Run your prompt in AI Mode, then look at each cited source and ask: what search would have surfaced this page? A cited “pricing” page implies a pricing sub-query; a cited comparison article implies a “vs” sub-query. The follow-up suggestions AI Mode offers are another strong hint about the angles it considers related. For more on how AI Mode picks its sources, see our Google AI Mode LLM SEO strategy.

3. Simulate fan-out with Gemini

You can ask a model to show its likely decomposition. It won’t be the exact production output, but it’s a fast way to brainstorm angles. Try this prompt in Gemini:

4. Mine your own search data

Real queries often reveal the same angles. Check Google Search Console for long, conversational queries your pages already get impressions for, and read People Also Ask boxes and related searches for your core topics. Where those overlap with what the extension and AI Mode show you, you’ve found a sub-query intent worth covering.

How to Optimize for Sub-Queries: A 5-Step Workflow

This is the workflow we use for clients in our generative engine optimization services. It turns a handful of buyer prompts into a clear content plan.

Step 1: Capture

Pick 10–20 prompts your buyers genuinely ask, the kind of full-sentence questions they’d type into ChatGPT, not two-word keywords. Run each one 3–5 times across ChatGPT, Google AI Mode and Perplexity, and log every sub-query and cited URL in a spreadsheet.

Step 2: Cluster by intent

Fan-out queries are probabilistic: the wording shifts on every run. Don’t chase exact phrases. Group the sub-queries into intents instead (“pricing”, “comparison with X”, “integrations”, “setup steps”, “local availability”). The intents that appear across multiple runs and platforms are the ones that matter.

Step 3: Map each intent to a page

For every intent cluster, decide which page should answer it and mark its current state: covered, thin or missing. Here’s what that looks like for a B2B SaaS company selling a CRM in India:

Illustrative example. Your own map comes from the sub-queries you capture in step 1.

Step 4: Cover the gaps with content worth citing

Now fill the “missing” and “thin” rows. What gets a passage picked is the same thing that makes it useful to a person:

• Answer the sub-query directly. Put the answer in the first sentence or two under a clear heading, then support it. Our guide on how to get cited in ChatGPT and Google AI Overviews shows this in practice.
• Be specific. Real prices, real limits, named integrations, dates, local details. AI engines prefer the page that settles the question over the page that talks around it.
• Cover comparisons honestly. “X vs Y” is one of the most common sub-query types. If you don’t publish a fair comparison, the AI will cite someone else’s.
• Keep facts current. Recency sub-queries reward pages with up-to-date years, prices and screenshots. Show a visible “last updated” date.
• Add proof. Original data, screenshots, case studies and expert commentary are what Google calls “non-commodity” content, the kind that can’t be found on ten other pages.
• Build off-site presence. Many sub-queries land on review sites, directories, Reddit threads and news articles. Being mentioned credibly there counts as much as your own pages.
• Link it together. Connect the pillar page and its supporting pages with descriptive internal links so crawlers and readers can follow each angle.

Step 5: Measure and repeat

Re-run the same prompts monthly. Track four things per prompt and platform: whether your brand is mentioned, whether your site is cited, which URL gets cited, and which competitors appear. For Google, also watch the generative AI performance report in Search Console. When a sub-query intent keeps going to a competitor, that’s your next piece of content.

What Not to Do: Fan-Out Myths Google Has Rejected

Plenty of “GEO hacks” are being sold as fan-out optimization. Google’s own guidance tells you to skip them:

• Don’t create a page for every sub-query. Dozens of thin pages targeting AI-generated phrasings will be ignored or flagged as low value. Cover intent clusters inside strong pages.
• Don’t chop content into tiny “chunks”. Google says its systems understand pages that cover several related topics. Write for readers with clear headings; that’s enough.
• Don’t rely on llms.txt or special AI markup. Google Search ignores them. Structured data is still useful when it accurately describes what’s visible on the page.
• Don’t chase exact sub-query wording. It changes every run. Optimize for the intent; Google itself says you don’t need to capture every variation of how someone might phrase a search.
• Don’t manufacture mentions. Fake reviews, paid listicles and planted forum posts are exactly the “inauthentic mentions” Google warns about.

Query Fan-Out Checklist

• ☐ 10–20 real buyer prompts documented
• ☐ Each prompt run 3–5 times on ChatGPT, AI Mode and Perplexity
• ☐ Sub-queries grouped into intent clusters
• ☐ Every cluster mapped to a page, marked covered, thin or missing
• ☐ Comparison, pricing and “2026” angles covered with specifics
• ☐ Pages crawlable, internally linked and showing a last-updated date
• ☐ Credible third-party mentions on review sites and directories
• ☐ Monthly re-test logged: mentions, citations, cited URL, competitors

Frequently Asked Questions

What is query fan-out in simple terms?

When you ask an AI search engine a question, it quietly runs several related searches (sub-queries) instead of one, then combines the best information from each into a single answer with citations. That splitting-and-merging process is called query fan-out.

Does Google confirm that it uses query fan-out?

Yes. Google Search Central documentation says both AI Overviews and AI Mode may use a query fan-out technique, issuing multiple related searches across subtopics and data sources to develop a response. Google first described the technique publicly when it launched AI Mode at Google I/O 2025.

How many sub-queries does an AI search engine generate?

It depends on the prompt and the platform. Google says AI Mode issues a multitude of queries and Deep Search can issue hundreds. In third-party studies, Peec AI measured an average of 1.4 fan-outs per query on Perplexity, 2.1 on ChatGPT and 6.8 on Grok (April 2026), while Nectiv Digital found ChatGPT averaging 7.61 fan-outs on a set of about 4,000 prompts (August 2026). Different prompt sets explain the gap.

Are fan-out queries the same as long-tail keywords?

No. Long-tail keywords are real searches that people type, with measurable search volume. Fan-out queries are synthetic: the AI model writes them on the fly, they change from one run to the next, and most have little or no search volume of their own. Optimize for the intents behind them, not their exact wording.

Can I see the exact sub-queries ChatGPT or Google AI Mode uses?

For ChatGPT, yes: its web-search queries can be captured with a fan-out browser extension such as the free Chrome extension from AI SEO Solutions. Google does not show AI Mode’s sub-queries directly, so you infer them from the sources it cites, the follow-up suggestions it shows and by simulating the prompt in Gemini.

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

No. Group related sub-queries by intent and cover each cluster inside a strong, well-organized page or a small topic cluster. Google says its systems understand pages that cover several related topics and that you can ignore tactics like chunking content into tiny pieces.

Do I need llms.txt or special markup to optimize for query fan-out?

Not for Google Search. Google’s guidance on optimizing for generative AI says you can ignore unnecessary AI text files such as llms.txt and other special markup. Standard SEO, crawlable text, accurate structured data that matches the page, and genuinely useful content are what matter.

How do I measure whether fan-out optimization is working?

Re-run the same set of buyer prompts every month in ChatGPT, Google AI Mode, Gemini and Perplexity. Track whether your brand is mentioned, whether your site is cited, which URL is cited and which competitors appear. For Google, also check the generative AI performance report in Search Console.