What are fan-out queries?
Fan-out queries are additional, related searches that a generative search system derives from a user's question to research subtopics in parallel and merge the results into one answer. Google describes the technique itself: at I/O on May 20, 2025, Google explained that AI Mode breaks a question into subtopics and issues many searches simultaneously. The Search Central documentation "AI features and your website" confirms that AI Overviews and AI Mode may use this "query fan-out" technique to find a wider range of supporting sources. Google's guide to optimizing for generative AI features (last updated July 10, 2026) defines fan-out as a set of concurrent, related queries and gives an example: a question about a lawn full of weeds turns into searches about herbicides, chemical-free weed removal and prevention. Google does not document how many sub-queries are generated. In our tests, reconstructed from visible sources and prompt analysis, we typically see 2 to 12 (own data, evidence level C).
The fan-out mechanic
An illustrative example (not published by Google, but derived from our prompt analyses): for a query like "What is the best CRM for the B2B mid-market?" the system may generate sub-queries such as "CRM vendor comparison B2B 2026", "CRM pricing for 100-500 employee companies", "CRM alternatives to Salesforce in the mid-market", "CRM integrations with HubSpot Marketing", "CRM implementation in DACH companies". Each sub-query returns its own results, from which the model assembles an answer with source attribution.
That has three consequences for SEO: (1) the main query is not enough; relevant sub-questions must be answered just as well. (2) Dedicated sub-pages make sense where a sub-aspect has standalone value for users, such as pricing or integrations; creating pages just for fan-out variants is treated as spam by Google (see below). (3) Internal linking between hub and sub-pages helps users and crawlers understand how the pages belong together; Google does not document it as a fan-out signal.
Sub-queries per main query in our tests (own data), depending on complexity
instead of a page factory: sub-pages only for aspects that stand on their own
as orientation for users and crawlers within the cluster
| # | Sub-query | Intent type | Content asset |
|---|---|---|---|
| 01 | CRM vendor comparison B2B 2026 | Evaluation | Comparison page (feature matrix) |
| 02 | CRM pricing for 100-500 employee companies | Pricing | Pricing deep-dive with tiers |
| 03 | CRM alternatives to Salesforce | Research | Alternatives page with migration info |
| 04 | CRM integrations with HubSpot / Slack | Technical | Integrations hub with vendor pages |
| 05 | CRM implementation DACH / EU | Regional | Implementation guide with GDPR section |
| 06 | CRM security / EU hosting | Compliance | Trust-center page |
| 07 | CRM customer stories & reviews | Social proof | Case studies + reviews hub |
| 08 | CRM 3-year TCO | Finance | ROI calculator + long-form |
Do you cover the sub-queries of your top category?
30-minute live mapping: we pull the fan-out chain of one of your most important main queries and show which sub-aspects still need content.
What does Google say about optimizing for fan-out queries?
Google explicitly warns against using fan-out as a page factory. Its guide to optimizing for generative AI features says, in essence, that creating separate content for every possible search variation, including fan-out queries, primarily to manipulate rankings or AI responses violates the scaled content abuse spam policy. The same guide states that content does not need to be broken into tiny chunks, that no special schema markup is required and that Google Search does not use dedicated AI text files such as llms.txt.
In practice, fan-out mapping is a coverage check. It shows which sub-questions your audience has and where your content has gaps. Answer those questions where it makes sense for readers, often as a section on an existing page, and create dedicated sub-pages only for aspects with standalone value.
Identifying sub-queries: three sources
1. Google People Also Ask. The PAA questions below the main query show which related questions Google considers relevant; Google does not document whether they match its internal fan-out queries. Tools like AlsoAsked expand PAA trees across several levels.
2. LLM self-analysis. Prompt Gemini, Claude or ChatGPT: "Which sub-questions must a comprehensive answer to this question address?" The models produce useful sub-query catalogues, which you should check against real user questions from sales, support and Search Console.
3. Search Console queries. Which long-tail queries already drive traffic to the hub page? Each one is a candidate sub-query.
Content architecture for fan-out
Hub-and-spoke structure: one hub page answers the main query at a high level with links to deeper sub-pages. Each sub-page answers a specific sub-query in depth with a clear back-link to the hub. Interlinking is explicit and semantically described (not just "read more" but "Deep dive on CRM pricing in the B2B mid-market").
Each sub-page with standalone value is optimized for its specific sub-question: its own title, its own H1, its own passage engineering. Where an aspect only fills two paragraphs, it belongs on the hub page as a section. The hub page is long but not monolithic — it summarizes and points deeper.
Conclusion: fan-out forces cluster thinking
If you want to be cited in AIO, AI Mode and ChatGPT, build your content around the real sub-questions of your audience. In our tests, a cleanly linked cluster of a hub page and a few sub-pages with standalone value was cited more often than a single page that only touches on every aspect (evidence level C). Google itself sets the limit: creating content for every fan-out variant primarily to manipulate results is spam.
Sources
- Google Blog: AI Mode in Google Search: Updates from Google I/O 2025 (May 20, 2025)
- Google Search Central: AI features and your website (continuously updated)
- Google Search Central: Optimizing your website for generative AI features on Google Search (July 10, 2026)
- Google Search Central: Spam policies for Google web search, section Scaled content abuse (continuously updated)