Fan-out queries are the internal trick generative search systems use to produce deep, nuanced answers to complex questions. Instead of retrieving a main query monolithically, Gemini (and ChatGPT Search analogously) internally generates between two and twelve sub-queries, runs a separate retrieval per sub-query and synthesizes the results together.
The fan-out mechanic
For a query like "What is the best CRM for the B2B mid-market?" Gemini internally generates 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 is retrieved separately, each returns its top results, and Gemini synthesizes a coherent answer with source attribution.
That has three consequences for SEO: (1) the main query is not enough — the sub-queries must be covered equally; (2) a single super-long page covering every sub-aspect performs worse than a network of specialized pages; (3) internal linking between hub and sub-pages becomes a structural lever — it signals semantic cohesion to Gemini.
Sub-queries per main query, depending on complexity
instead of a super page — coherent content clusters
as a semantic signal for cluster cohesion
| # | 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.
Identifying sub-queries: three sources
1. Google People Also Ask. The four PAA questions below the main query are Google's public sub-query preview. 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 excellent sub-query catalogues, because they essentially mirror the fan-out mechanic in their own retrieval logic.
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 is optimized for its specific sub-query: its own title, its own H1, its own passage engineering. The hub page is long but not monolithic — it summarizes and points deeper.
Conclusion: fan-out forces cluster thinking
Anyone who wants to be cited inside AIO, Gemini and ChatGPT must build their content in fan-out-compliant clusters. A single super page covering every sub-aspect remains below the top sub-query results in the synthesis logic. A cleanly linked cluster with a hub page plus 5-8 sub-pages is cited systematically better.