Query Fan-Out (roughly translated as ‘query fan-out’) is an information retrieval technique whereby AI search systems break down a single search query into several thematic sub-queries in the background, execute these in parallel and synthesise the results into a single, coherent response. Google uses this method as the central mechanism of its AI Mode.
Instead of searching for the entered words just once, the system generates a bundle of related queries from the original question, each covering a different aspect of the user’s intent. For example, the query “which shop system for B2B wholesale with ERP integration” is broken down in the background into: B2B functions of shop systems, ERP interfaces in e-commerce, platform comparisons for wholesale, typical costs and migration effort. Each of these sub-queries is answered separately, and the model constructs a coherent overall response from the results.
How Query Fan-Out works technically
Google publicly described the process with the launch of AI Mode (officially announced in May 2025). A specially trained Gemini model generates the sub-queries; a typical query generates around 8 to 12 of these, whilst complex searches in Google’s ‘Deep Search’ can generate hundreds. Elizabeth Reid, Head of Google Search, has described Query Fan-Out as the mechanism that makes the intelligence of AI Mode possible in the first place.
To ensure the parallel searches work, dense vector retrieval runs in the background: each sub-query and every relevant text passage is translated into a vector, and the system searches for semantic proximity rather than exact word matches. The most relevant passages from all the sub-searches are extracted, grouped by topic and combined into a single answer. The user sees nothing of this complex process — only the finished result with a few linked sources.
Visible and invisible queries
Crucially, most of these sub-queries remain invisible to the user. They ask a question and receive an answer; they are unaware that a dozen or more searches took place behind the scenes. This has far-reaching implications for a website’s visibility: the site no longer needs to match the original query exactly, but rather one of the many derived sub-queries.
Implications for SEO, e-commerce and B2B
Query fan-out is changing the very foundations of search engine optimisation. Anyone who optimises solely for that one, obvious main keyword will, in the fan-out model, cover only a small fraction of the queries actually made. Success comes to those who cover a topic in its entirety: with content that independently answers the obvious detailed questions, comparisons, costs, prerequisites and special cases.
For online shop operators and B2B providers, this results in a clear content strategy:
- Topic clusters instead of single keywords: A main topic is flanked by numerous, closely related subpages and sections that cover typical fan-out sub-queries.
- Question-oriented headings: H2/H3 headings formulated as genuine questions address the derived sub-questions more precisely than generic keywords.
- Standalone passages: Each section provides a complete answer to a specific sub-query, so that it can be quoted as a standalone chunk.
- Structured data: Schema.org markup helps the system assign content to the correct sub-queries.
A concrete example
A manufacturer of technical components wants to be visible for procurement decisions. Instead of a single page titled ‘Buy industrial components’, they build a cluster: dedicated, in-depth sections on technical specifications, delivery times, minimum order quantities, ERP integration for orders, certifications and volume discounts. If a buyer in AI Mode asks a complex question, the system breaks it down into precisely these sub-questions — and the manufacturer has a quotable passage ready for several of them. It is precisely this behaviour that Google documents in its official search announcement regarding AI Mode.
Common misconceptions and related concepts
One misconception is that query fan-out is simply a new name for the well-known keyword research. In fact, it is an automated process that takes place at search runtime: the model generates the sub-queries dynamically and semantically, not from a fixed list of keywords. A second misconception is that one can know the exact sub-queries and specifically ‘target’ them. As they are generated based on the model and context, they can only be anticipated approximately — the correct approach is thematic depth, not guessing individual strings.
Closely related are Dense Retrieval and vector embeddings (the technical basis), Generative Engine Optimisation (GEO) and Relevance Engineering as an overarching discipline. The concept of passage extraction (chunking) is also directly linked to query fan-out, as the partial answers are assembled at the passage level.
Outlook
Query Fan-Out exemplifies the shift in search from a list of blue links towards synthesised answers. With more powerful models, the number and quality of sub-queries will continue to rise, particularly in agent-based and ‘Deep Research’ scenarios, where systems conduct research autonomously. For websites, the strategic focus remains the same: thematic completeness, passages that can be quoted in their own right, and a clear structure. A detailed explanation of the process is provided in the Query Fan-Out Guide by Search Engine Land.
From Passage Indexing to Query Fan-Out
Query Fan-Out did not emerge out of thin air, but is the logical evolution of several steps. As early as 2020, Google began evaluating individual passages on a page independently of the rest (passage ranking) in order to find answers buried deeper within the content. At the same time, search shifted from pure word matching towards semantic understanding, driven by language models and vector representations. Query Fan-Out combines both: it uses semantic understanding to turn one question into many, and the passage level to piece together the partial answers.
The method thus forms part of a longer-term development in which search is returning less and less of a list of documents and increasingly constructing a composite answer. In practice, this means that the unit for which competition takes place is no longer the page, but the passage that matches a sub-query.
Query Fan-Out compared to traditional search
The difference from traditional search can be identified across several dimensions:
| Dimension | Traditional search | Query fan-out |
|---|---|---|
| Number of searches per query | one | typically 8–12, in Deep Search hundreds |
| Hit logic | Word matching, ranked list | Semantic vector proximity, parallel sub-searches |
| Result | List of blue links | Synthetised answer with few sources |
| Optimisation objective | Ranking for a keyword | Results for many sub-queries, citation |
| Content structure | page-by-page, keyword-centred | modular, question-oriented, self-contained passages |
The most important practical implication arising from this comparison is: Anyone wishing to be visible in the fan-out model must provide thematic completeness. It is not enough to target the one obvious main keyword; what is required is content that answers the context of the query — comparisons, costs, prerequisites, special cases — independently and with supporting evidence in each instance.
Common mistakes when optimising for query fan-out
Anyone who understands query fan-out avoids certain typical mistakes rooted in traditional keyword thinking. The most common is focusing exclusively on a single main keyword: in the fan-out model, a single, heavily optimised page covers only a fraction of the sub-queries actually asked. A more effective approach is a cluster of content-related pages and sections that cover the topic in its entirety.
A second mistake is keyword stuffing in the hope of achieving exact word matches. As sub-queries are processed via semantic vector proximity, the mechanical repetition of terms no longer offers any advantage; rather, it detracts from readability. Thirdly, many underestimate the technical requirements: if an AI crawler cannot read the content at all due to a lack of server-side rendering, even the best breadth of content is of no use. Fourthly, passages are written in a way that lacks independence — a section that derives its meaning solely from the preceding text cannot be clearly extracted as a partial answer.
The remedies in all four cases are the same principles: thematic depth rather than a fixation on keywords, semantically rich language rather than mechanically repeated phrases, a technically readable foundation, and self-contained, question-oriented passages. In this way, an understanding of the fan-out mechanism is transformed into a concrete, actionable content strategy.
Frequently asked questions about Query Fan-Out
How many sub-questies is a query broken down into?
A typical query in AI Mode generates around 8 to 12 sub-questies. In ‘Deep Search’ for complex queries, there can be hundreds, according to Google. The exact number depends on the complexity of the question.
Can I see the generated sub-queries?
Generally not. The sub-queries run in the background; the user only sees the composite answer and a few linked sources. Tools attempt to replicate typical fan-out patterns, but can only approximate them.
What does query fan-out mean for my SEO strategy?
Don’t just optimise for a single main keyword; cover a topic in its entirety. Question-oriented headings, self-contained, quotable sections and a dense cluster of topics increase the chance of being cited as a source for one of the derived sub-queries.
What technology lies behind Query Fan-Out?
Google uses a Gemini model specially trained for fan-out. The parallel sub-searches are carried out via dense vector search, in which queries and passages are compared as vectors. The results are then synthesised into a single answer.
Does Query Fan-Out only apply to Google?
The principle of breaking down a complex query into several sub-queries and synthesising the results can be found in a similar form in other AI search systems and research agents such as Perplexity or ChatGPT with a search function. The term ‘Query Fan-Out’ is closely associated with Google’s AI Mode, but the underlying pattern is more general and shapes generative search as a whole.
How do I prepare my shop for Query Fan-Out?
Firstly, ensure that AI crawlers can actually read your content (server-side rendering, no blocking via robots.txt). Then build topic clusters with independently quotable passages covering all relevant detailed questions relating to your products, and mark up products and attributes using structured data. This increases the likelihood of your site serving as a source for several of the derived sub-queries.
How is Query Fan-Out related to Relevance Engineering?
Query Fan-Out is one of the reasons why Relevance Engineering became necessary in the first place. Because a query breaks down into many semantic sub-queries, optimising for a single keyword is no longer sufficient. Relevance Engineering addresses this with independently citable passages, a clean entity structure and thematic depth – precisely the building blocks that content needs in order to be considered as a source for as many of the fan-out sub-queries as possible.