Query fan-out is used by AI search systems to break down a user's search query into multiple parallel sub-queries and combine their results into a generated response.
Query fan-out is the retrieval stage within RAG-Systems (read here: Retrieval Augmented Generation).
Instead of just matching a query („search“) against a list of results, a generative model creates multiple intent-different sub-queries, runs them in parallel, and builds the response incrementally from the results.
This is mainly triggered by complex, comparative, or multi-criteria queries. However, it's less likely for factual questions.
For content creation, this means: a page no longer competes for a single keyword, but rather for whether it is the best source of passages for multiple parallel sub-intents. The Keyword research thus turns into intent analysis.
Differentiation from the similarly named „fan-out“
The term clashes with two established meanings: „fan-out“ from software development and messaging (a distribution pattern where one message goes to multiple recipients), and „fan-out“ from electronics (the number of inputs an output can drive). The confusion is understandable because all three share the same imagery—one source spreading out to many destinations. However, this context exclusively refers to the retrieval method in AI search: same name, different subject. Anyone searching for the messaging or electronics concept is in the wrong place.
How a query fan-out works (and when it's triggered at all)
The process follows four steps:
- Analyze The system analyzes the request with NLP models for intent, complexity, and required response type.
- Decision The system decides whether fan-out is useful.
- Activation: Upon activation, a generative model produces parallel sub-queries. These run concurrently against multiple data sources, such as live web searches, knowledge graphs, potentially shopping graphs, or vertical indexes.
- Generation: The answer is then not generated from entire pages, but from passages: Relevant „chunks“ from the retrieved documents are selected and woven into the answer.
Depending on the AI system, not every request triggers a fan-out. Factual short questions like „What is the capital of Spain?“ are usually answered directly.
In contrast, complex, comparative, or multi-criteria questions activate the mechanism: „How do I optimize performance for Website“
Google has the procedure at I/O 2025 officially described: AI Mode break down the question into subtopics and ask „a multitude of queries simultaneously“ (see also I/O 2025 Takeaways).
Google's Deep Search utilizes the same technique in a scaled-up form and, according to its own statement, can perform „hundreds of searches“ per user query.
8 Types of Google Sub-Queries
Google itself divides a user's query into 8 sub-query types.
This is for SEO This is exciting because you can also draw conclusions for content creation from the respective sub-query type in order to perfectly cater to the exact search intent of users.
For the origin of the typology, a brief derivation is important beforehand: The formal mechanism originates from two Google patents.
- Query Variant GenerationUS11663201B2)
- Thematic SearchUS12158907B1)
Google itself has only been publicly using the term „query fan-out“ since I/O 2025. In the patent text linked above, it is still called "query variant generation.".
However, the eight variant types mentioned in US11663201B2 are different from those commonly used in SEO practice. The patent itself lists equivalent, follow-up, generalization, canonicalization, language translation, entailment, specification, and clarification.
The following eight types are not the patent wording but an evaluation established in the SEO community (e.g., iPullRank, Wellows) that is based on the patents and translates them for content practice.
This eight-type logic provides a reproducible framework for checking which branches a page already covers and which are missing:
- ReformulationThe same intention, different wording („CRM software“ → „customer management tools“).
- ImplicitImplicitly implied context that the user doesn't state („CRM software“ -> „CRM software for small teams“).
- Entity ExpansionExtension with connected entities, brands, products („CRM Software“ → „Salesforce vs. HubSpot“).
- ComparativeComparison with alternatives, „vs.“ requests, selection decisions.
- PersonalizedVariants depending on role, industry, experience level („CRM for Mechanical Engineering“, „CRM for Beginners“).
- RelatedThematically related questions about the seed.
- Definitional / CategoryWhat-is-Questions and Categorizations.
- EquivalentSynonym or semantically equivalent phrasing.
The typology only becomes meaningful when it is tested with a concrete seed.
An everyday example with the search query (seed) „buy running shoes“:
- Reformulation = „What running shoes should I buy“ — same intent, different words.
- Implicit Running shoes for beginners.
- Entity Expansion „Nike Pegasus vs. Adidas“ — specific brands and models implied.
- Comparative Running Shoes vs. Hiking Boots — Differentiating from the Alternative.
- Personalized Running shoes for flat feet - variation by personal characteristic.
- Definitional = „What makes a good running shoe“ - the what/fundamentals question.
- Equivalent „Running shoes“ - same meaning, different word.
Important for managing expectations: The specific sub-queries generated are not stable. The eight mentioned types are reproducible, but the exact phrasing is not. This is because AI systems are so-called black-box systems.
What Query Fan-Out means for content creation
If a query breaks down into several sub-queries and the AI selects passages, the visibility is no longer determined by the entire page, but by individual sections. This results in a structural logic that differs from classic keyword optimization and creates three recurring pitfalls:
- to create one page per phrasing variant
- to engage in keyword stuffing
- or fan-out as an extended keyword list
A viable page structure answers the main question in a condensed form (TL;DR block), then outlines by H2 per intent branch (definition, comparison, use case, delimitation) and addresses implicit and comparative sub-queries in their own sections, not in subordinate clauses.
Important: Always keep the user in mind, not the AI search engine.
What does the user really want to know? That's exactly what Query Fan-Out does too.
Consolidate, don't fragment
The urge to create a separate page for every phrasing variation is usually not productive. This fragments thematic depth and distributes relevance signals across multiple thin pages.
It is better to create a main page that answers several sub-query branches in clearly structured sections. Sub-topics that are self-contained enough (e.g., a complete comparison or an industry application) are moved to their own pages (and linked from the main page).
The rule of thumb is: consolidation is the default; fragmentation requires justification beyond phrasing.
Consciously use comparison and clarification subqueries
The two sub-query types, Comparative and Implicit, belong to the so-called decision-proximate sub-query branches because they typically arise during the decision-making phase. They are regularly overlooked.
Those who describe „X“ without addressing „X vs. Y,“ „When is X worth it,“ or „X for [role/industry]“ leave those branches to other sources.
In practice, this means: at least one explicit comparison section per main page and a section on typical context assumptions (company size, use case, prerequisites).
FAQ blocks are a suitable format for this, as long as the questions come from actually observed sub-queries and do not serve as filler material.
Why Traditional Visibility Measurement Fails with Fan-Out
Subqueries from fan-out processes do not appear in Google Search Console. Only the original user query is visible there, not the eight to twelve derived subqueries that internally led to the answer. Anyone who measures visibility in AI answers solely by classic GSC metrics will not see the mechanism that determines visibility at all.
Then there is the issue of stability: Since only about 27% of subqueries remain the same when repeated, it is not economically viable to optimize exhaustively for specific subquery formulations.
A more practical approach is a two-stage process: for strategically important seed terms, one simulates the actual fan-out using so-called fan-out generators, AI trackers, and visibility tools, which capture sub-queries from AI Mode, Perplexity, or ChatGPT.
For breadth, thematic authority suffices as a proxy: a page that thematically covers the eight sub-query types along the seed has a higher probability of being selected as a source in multiple parallel retrieval runs.
How strong this effect is, shows a Surfer SEO Study over 173,902 URLs and 10,000 keywords (November 2025).
She found a correlation (Spearman's r = 0.77) between the number of fan-out queries for which a page ranks and its likelihood of being cited in AI overviews. Pages that ranked for the main term and at least one fan-out query were cited 161 % more frequently than pages that ranked only for the main term. The same study also provides the stability figure mentioned above: only about 27% of the sub-queries remain constant across repeated runs. A complementary metric is citation in AI responses themselves—how often a domain appears as a source for defined prompts. A Semrush experiment observed an increase from two to five citations in a small sample of four articles following targeted fan-out optimization; the magnitude serves as a useful indication, not causal evidence.
What else counts in the B2B and DACH context
Two effects are more pronounced in the B2B environment than in the consumer segment.
First, fan-out for DACH inquiries often runs in parallel in German and English because a significant part of the technical terminology is English. For example, „Predictive Maintenance,“ „Condition Monitoring,“ or „Digital Twin.“.
A German-language website that exclusively uses translated central technical terms does not cover English sub-queries, and vice versa. Consistent dual mention in the running text (English technical term plus German equivalent) and both variants consistently in headings and FAQs are useful.
Secondly, personalized and comparative sub-queries are highly differentiated in B2B. For solutions requiring explanation, sub-queries arise that explicitly ask about role (maintenance manager, IT manager, purchasing), industry (mechanical engineering, process industry, energy), and maturity level (pilot project, rollout, existing system). A main page that only describes the solution, without catering to these role and industry branches, is missing precisely in the sub-queries that arise during the decision-making phase. The structural answer: role and industry sections as fixed building blocks in the page structure and not as optional add-ons.