TL;DR
- Google named query fan-out when it launched AI Mode in Search Labs in March 2025 and expanded on it at Google I/O in May 2025; AI Overviews and AI Mode may both use it.
- Deep Search in AI Mode applies the same idea at larger scale, running hundreds of searches for one research-style request.
- Google says AI features have no extra technical requirements: a page must be indexed and eligible to show with a snippet.
- Search Console counts AI feature clicks inside the regular Web performance report, so citations need a separate prompt-panel check.
Query fan-out is the technique Google's AI search features use to answer one question by splitting it into many smaller searches, running them at the same time, and combining what they find into a single response. For publishers, it means a page can be cited in an AI answer because it answers one sub-question well, even if it does not rank for the phrase the user typed.
Google first named the technique in March 2025, when it introduced AI Mode as an experiment in Search Labs, and explained it further at Google I/O in May 2025, when AI Mode began rolling out to all users in the US. Its Search Central documentation on AI features says both AI Overviews and AI Mode may use query fan-out to build a response. This guide explains how the process works, why it changes which pages get cited, how to research the sub-queries a topic is likely to generate, and how to measure the results honestly.
What query fan-out is in AI Mode and AI Overviews
In classic search, one query produces one ranked list. The user types a phrase, Google matches pages to that phrase, and the user picks a result. If the question is complicated, the user does the decomposition: they search several times, open several tabs and stitch the answer together.
Query fan-out moves that work to the system. When someone asks AI Mode a layered question, the model first breaks it into subtopics, then issues a set of related searches across those subtopics and across different data sources, then reads the results and writes an answer that links to the pages it drew from. Google has said that its Deep Search option in AI Mode takes the same idea further, running hundreds of searches for a single research-style request.
AI Overviews, which appear above the regular results for some queries, can use the same technique. The practical result is the same in both: the answer is assembled from many retrieval steps, and the supporting links come from whichever pages best answered each step.
How one prompt becomes many sub-queries
Take an illustrative prompt: "Which CRM is best for a 20-person B2B sales team in Bangkok that already uses LINE?" A single search for that sentence would return weak matches, because few pages are written for exactly that situation. A fan-out system can split it into narrower searches, for example:
- CRM features that matter for small B2B sales teams
- CRMs with LINE integration
- CRM pricing per user for teams of around 20
- CRMs with Thai-language interfaces or local support
- how to compare CRM implementation effort
Each sub-query is something a page could answer directly. The system retrieves results for each, reads them, and composes one response that covers features, integration, cost and support. The list above is an example of the pattern, not a record of what Google actually generated, because Google does not publish the sub-queries it runs.
Two properties of these fan-out sub-queries matter for planning. First, they are written by the system, not typed by a person, so many of them will never show up in keyword tools with measurable search volume. Second, they depend on context: the wording of the prompt, earlier turns in the conversation and the kind of answer the system decides to give. The same topic can fan out differently for different users.
Why pages that rank for sub-topics get cited
Because the answer is built from many retrieval steps, the competition happens at the level of each sub-question. A page that is the clearest answer to "CRMs with LINE integration" can be cited in the final response even though it would never rank for the full original prompt. Google has said AI features can show a wider and more varied set of supporting links than a classic results page, which follows from this design.
This shifts what "visibility" means. A broad page that mentions everything in passing may lose to narrower pages that each answer one sub-question with specifics. A page that answers a sub-question in a self-contained passage, with the fact stated plainly near a descriptive heading, gives the system something it can lift and attribute.
It does not mean the fundamentals change. Google's documentation says there are no extra technical requirements for appearing in AI Overviews or AI Mode. A page needs to be indexed and eligible to show in Search with a snippet, and the usual guidance on helpful, reliable content applies. Fan-out raises the reward for covering sub-questions well; it does not create a separate ranking system you can game with special markup.
How to research likely fan-out queries for a topic
Since the real sub-queries are hidden, research is about predicting the questions a system would need answered to respond well. A practical process:
- Decompose the topic yourself. Write the main question, then list what a complete answer must cover: definitions, how it works, options and comparisons, costs, steps, risks, local factors and alternatives. These facets map closely to the kind of sub-queries a fan-out system generates.
- Mine the results page. People Also Ask boxes, related searches and autocomplete suggestions show the follow-up questions real users have about the topic. They are an imperfect but useful proxy for sub-queries.
- Read your own Search Console data. Filter queries for question words and long phrasing. Longer, conversational queries hitting your pages show which sub-questions you are already being considered for.
- Ask the AI features directly. Put the main question to AI Mode or another answer engine, note which subtopics the response covers, and note which sources it cites for each part. Repeat with a few rewordings. The pattern across runs is more reliable than any single answer.
- Map gaps. For each sub-question, ask whether your site has a passage that answers it directly. A missing passage is a content gap; a buried one is a structure problem.
Some SEO tools now offer features that simulate fan-out for a seed query. Treat their output the same way as your own decomposition: as a list of plausible sub-questions to check, not as data about what Google ran.
Content structure that covers sub-questions
The goal is a page where each sub-question has an obvious, self-contained answer. Some structural habits help:
- Use headings that state the sub-question or the thing being answered, so the passage underneath is clearly about one point.
- Put the answer in the first sentence under the heading, then add the detail. A passage that needs the previous three paragraphs to make sense is hard to cite.
- Use tables for comparisons, specifications and step outcomes. They hold many facts in a compact, extractable shape.
- Name things precisely. Say which product, which setting, which market and which date, instead of "this tool" or "recently".
- Keep facts current and dated where they can change, such as prices, limits and availability.
- Link related pages so a cluster covers the topic: one main guide plus focused pages for sub-topics that need more depth.
A question-and-answer section at the end of a page is useful for the narrow follow-ups that do not deserve their own heading. Write each answer so it stands alone, because the system may only read that part.
Fan-out compared with classic long-tail keyword research
Long-tail research and fan-out planning look similar, since both deal with specific, multi-word questions. The difference is who writes the query and how you can see it. The table below sets out the main contrasts.
| Aspect | Classic long-tail research | Query fan-out planning |
|---|---|---|
| Who writes the query | A person types it into the search box | The AI system generates it from a broader prompt |
| Visibility in keyword tools | Often has measurable search volume | Mostly invisible; many sub-queries have no reported volume |
| What wins | A page ranking for that phrase | A passage that answers one sub-question clearly |
| How you measure it | Rank tracking and clicks per keyword | Citations across a set of test prompts, plus traffic trends |
In practice the two work together. Long-tail data tells you which questions real people ask. Fan-out thinking tells you to answer them as discrete, citable passages inside a well-organised cluster, rather than producing one thin page per phrase.
How to measure citations from fan-out
Measurement is the weakest part of the picture, and it is better to say so than to report numbers that do not mean what they seem to mean.
For Google, Search Central documentation places clicks and impressions from AI features inside the regular Search Console Performance report, under the Web search type, rather than in a separate AI filter. At the time of writing you cannot isolate AI Mode or AI Overview traffic cleanly in that report, so check whether this has changed in your property before building a dashboard around it. What you can watch is the trend in long, conversational queries and in impressions for pages that cover sub-topics.
For other answer engines, referral traffic shows up in GA4 under the assistant's own domain as the referrer, which lets you see visits from assistants that link out. That only captures people who clicked, not how often you were cited.
To track citations directly, build a fixed panel of prompts for your topic: the main question plus several realistic variations. Run the panel on a regular schedule, record whether your pages are cited and for which part of the answer, and compare over time. Responses vary between runs, so look at the share of runs in which you appear, not at a single result.
What this means for Thai marketers
Thai search adds a wrinkle. When a query mixes Thai and English, such as a Thai question with an English product or software name, a fan-out system may retrieve sources in either language. Where Thai-language coverage of a sub-question is thin, an answer may lean on English pages, which leaves room for a Thai page that answers that sub-question clearly and specifically for the local market.
Local facets are natural sub-questions for Thai topics: prices in baht, availability in Thailand, Thai regulations, local payment and messaging platforms such as LINE, and Thai-language support. Pages that answer these directly cover the parts of a fan-out that global content often skips. For a structured approach to this kind of visibility, see generative engine optimisation.
FAQ about query fan-out
Is query fan-out only a Google feature?
The name comes from Google, which uses it to describe how AI Mode and AI Overviews build answers. The planning approach, covering sub-questions with clear, self-contained passages, also suits any AI assistant that searches the web before it answers.
Do I need special markup to appear in fan-out results?
No, Google's documentation says there are no additional technical requirements beyond being indexed and eligible to show with a snippet. Structured data that matches visible content is still good practice, but it is not a separate entry ticket for AI features.
Can I see the sub-queries Google runs for my topic?
No, Google does not publish the sub-queries it generates. You can approximate them by decomposing the topic, studying People Also Ask and related searches, reviewing long queries in Search Console and testing prompts in the AI features themselves.
Should I create a separate page for every sub-question?
Usually not, because thin pages for each phrase tend to duplicate each other. Answer most sub-questions as clear sections of a main guide, and give a sub-topic its own page only when it needs real depth of its own.
Where to start
Pick one topic that matters commercially, write out the sub-questions a complete answer would need, and check your site passage by passage against that list. Fix the missing and buried answers first, then set up a prompt panel so you can see whether citations move. If you want help building that kind of coverage, see AI SEO services, ChatGPT SEO for visibility in assistant answers, or content marketing for producing the cluster itself.
