--- title: "Query fan-out: definition and example – Carl-Uwe Würfel" description: "Query fan-out explained: how Google AI Mode and AI Overviews split one question into many searches, how that differs from classic search, and what follows." url: https://carl-wuerfel.de/en/glossary/query-fan-out author: Carl-Uwe Würfel --- 1. [Home](https://carl-wuerfel.de/en) 2. [Knowledge](https://carl-wuerfel.de/en/knowledge) 3. [Glossary](https://carl-wuerfel.de/en/glossary) 4. Query fan-out # Query fan-out Query fan-out is an AI search technique: the model breaks a question into subtopics, runs several related searches at the same time and combines the results into one answer. ## On this page 1. How a fan-out query runs 2. How fan-out differs from classic search 3. Where the idea comes from 4. What it means for content 5. Common misconceptions 6. How to observe the effect 7. Where the term stops working 8. Frequently asked questions 9. Sources Fan-out is the step that turns one question into many searches. _Figure: A branching diagram based on Google 's own example from its documentation for site owners. The user asks how to fix a lawn full of weeds. From that, the model itself generates several related searches that run concurrently: for lawn herbicides, for removing weeds without chemicals and for preventing weeds. The user sees only the finished answer with links. Google publishes neither the queries generated in a given case nor how many there are._ **Example** A maker of shelving systems notices that for questions like “What shelving works in a cold store with a low ceiling?” AI Mode mostly links to dealer sites and forums. Its product pages list only dimensions and load capacities; details on cold, fire safety and minimum clearances sit in a PDF manual. It collects the questions sales and service hear most and adds sections that answer them in plain text. It tracks the effect over months in Search Console and with fixed test questions. Google’s documentation for site owners defines query fan-out as a set of concurrent, related queries that the model generates to request more information and fetch additional relevant results for the user’s question. Google’s example: a question about fixing a lawn full of weeds fans out into searches for lawn herbicides, removing weeds without chemicals and preventing weeds. The user sees only the finished answer with links. Google introduced the term publicly on March 5, 2025, when it announced AI Mode, which it said issues multiple related searches concurrently across subtopics and multiple data sources and brings the results together into an easy-to-understand response. In May 2025 came Deep Search, which Google says uses the same technique to issue hundreds of searches. The German launch announcement of October 8, 2025 calls it the “Query-Fan-Out-Verfahren.” According to Search Central, both AI Mode and [AI Overviews](https://carl-wuerfel.de/en/glossary/ai-overviews) may use it (as of November 2026). For companies, query fan-out changes what a page gets found for. Instead of one query typed by a person, the system checks many sub-questions nobody typed. A page can show up as a source because it answers one of those sub-questions well. At the same time, Google explicitly warns against building separate pages for every possible sub-query. ## How a fan-out query runs. The language model works out which subtopics a question contains and writes its own search queries for them. They run at the same time, against the search index and, according to Google, other data sources. According to Google, its existing Search ranking systems decide which pages fit a sub-query. The documentation describes the process as [retrieval-augmented generation](https://carl-wuerfel.de/en/glossary/rag), or RAG, also called [grounding](https://carl-wuerfel.de/en/glossary/grounding): core Search systems retrieve relevant, current pages from the index, then the model reviews the specific information on those pages and writes an answer that links to the pages supporting it. Fan-out is the step that turns one question into many searches. Google does not disclose how many sub-queries are generated. For Deep Search it speaks of hundreds of searches, for regular AI Mode of a multitude. The documentation only says that AI Overviews and AI Mode may use the technique; it does not say whether a simple question about opening hours triggers it. The result is a text with citations. Google says the technique lets it find more supporting pages and show a wider, more diverse set of links than a classic web search. ## How fan-out differs from classic search. In classic search, a person types a query and gets a ranked list of results. If the question has several parts, the person splits it up: one search for prices, one for reviews, one for alternatives. With query fan-out, the system does that splitting. The user asks once, many searches run in the background, and a summary with links comes back. Query expansion, an older term in information retrieval, is something else. It adds synonyms or related terms to a single query so it matches more relevant documents. It is still one search with one result list. Fan-out creates separate queries on different subtopics, retrieves them separately and only merges the results in the answer. Looking at parts of a page is not new: Google’s ranking systems guide lists a passage ranking system that identifies individual sections of a page to judge their relevance to a search. What is new is that a single question sets off many such searches and the user reads only the combined result. ## Where the idea comes from. The name comes from Google; the idea is older. A well-known example is self-ask, from a paper by Ofir Press and colleagues first published in October 2022: before answering, the model asks itself follow-up questions and answers them. The authors show that a search engine can be plugged in to answer those follow-up questions, which further improves accuracy on multi-step questions. In the literature, splitting a question into sub-questions is usually called query decomposition. Decomposition describes the logical breakdown, often in sequence, where one partial answer shapes the next question. Fan-out stresses breadth: many related searches at once, with the results merged afterward. The line is blurry. Other AI search services describe their process in less detail and do not use the term consistently. What Google says about query fan-out applies to Google Search. Whether another service splits questions the same way can only be inferred from its own documentation or from observation. ## What it means for content. Google asks for nothing extra to appear in AI Overviews or AI Mode. A page has to be indexed and eligible to appear in Search with a snippet. New machine-readable files, AI text files or special markup are not needed, according to Search Central. Google’s guide to optimizing for generative AI features rejects several assumptions tied to fan-out. Content does not need to be cut into tiny chunks; Google’s systems recognize several topics on one page and show the relevant part. Nor does text need rewriting for AI, since the systems understand synonyms and meaning. What remains is editorial work. Google recommends content that brings a unique viewpoint or first-hand experience instead of restating what is already out there, organized in sections with clear headings. For fan-out, a simple inference follows: a page that actually answers its customers’ questions, with details that are missing elsewhere, has more chances to match a sub-question. This is not a documented rule; it follows from how the technique is described. Prices, scope and limits of use belong on the page as text, not locked in images or PDF files. ## Common misconceptions. The sub-queries can be read out. Google publishes neither the generated queries nor how many there are. Tools that display “fan-out queries” simulate them with their own models. According to Google, no third-party tool has access to its internal ranking or AI systems. Every sub-query needs its own page. Google says the opposite. Creating separate content for every possible variation, explicitly including fan-out queries, primarily to manipulate rankings or AI responses violates its scaled content abuse spam policy. A large number of pages, Google says, makes a site neither higher quality nor more relevant. Content has to be cut into short blocks. According to Google there is no such requirement and no ideal page length. A good ranking guarantees a citation. Because the answer draws on many sub-searches, a page that ranks well for the original query can be missing while another page is cited for one sub-question. Query fan-out is the same as RAG. RAG is the broader principle of tying an answer to retrieved sources. Fan-out is the step of turning one question into many searches. ## How to observe the effect. Query fan-out cannot be measured directly, because the sub-queries stay hidden. What can be observed is the outcome: whether and how often a site appears as a source in AI answers. Since August 31, 2026, Search Console has offered all sites worldwide a performance report for generative AI features in Google Search. It counts impressions from AI Overviews and AI Mode: how often links to the site were shown there. The help page lists pages, countries, dates and devices as dimensions. It does not describe a breakdown by query. So it does not reveal which sub-query led to a citation, but it does show which pages appear in AI features. Spot checks fill in the picture. A fixed list of customer-style questions is put to AI Mode at regular intervals, and the cited sources are recorded. Answers vary by location, history and timing, but patterns still emerge: which subtopics competitors or forums own and where the site is missing. Together, both yield a working list: questions the site leaves unanswered, and pages that appear in AI features with outdated information. ## Where the term stops working Query fan-out describes how Google’s AI search searches, not how to optimize; it is neither a ranking factor nor a method for site owners. It differs from RAG, the broader principle of tying answers to retrieved sources, from query expansion, which adds synonyms to a single search, and from [GEO](https://carl-wuerfel.de/en/geo), the work of earning visibility in AI answers. What holds for Google’s technique does not automatically hold for ChatGPT, Perplexity or other services. ## Frequently asked questions What is query fan-out? Query fan-out is a technique Google uses in AI Mode and AI Overviews to break a question into subtopics and run several related searches at the same time. The results feed a summarized answer with links. The user does not see the sub-queries. Where does the term query fan-out come from? Google used the term publicly when it introduced AI Mode on March 5, 2025, and again for Deep Search in May 2025. The idea of splitting a question into sub-questions answered through search is older, for example the self-ask method from 2022. Do AI Overviews use query fan-out too? Yes, according to Google both AI Overviews and AI Mode may use the technique. The documentation does not say which queries trigger it. Google describes it most clearly for AI Mode and Deep Search. How many sub-queries does query fan-out generate? Google does not give a fixed number. For AI Mode it speaks of a multitude of simultaneous queries, for Deep Search of up to hundreds of searches. By that description, the number depends on the kind of question. Can you see the fan-out queries for a website? No, Google does not publish the sub-queries. The Search Console report on generative AI features shows impressions by page, country, date and device, but no queries. Tools that display fan-out queries estimate them with their own models. Should you create a page for every fan-out query? No. Google treats it as a violation of its scaled content abuse spam policy when pages for every query variant, explicitly including fan-out queries, are created primarily to manipulate rankings or AI responses. Pages that fully answer real customer questions are the better route. What is the difference between query fan-out and query expansion? Query expansion adds synonyms or related terms to a single query and still returns one result list. Query fan-out creates several separate queries on different subtopics, retrieves them separately and merges the results into one answer. ## Further reading * [**AI glossary**](https://carl-wuerfel.de/en/glossary) * [**GEO: measuring AI visibility**](https://carl-wuerfel.de/en/geo) * [**Google AI Mode**](https://carl-wuerfel.de/en/glossary/ai-mode) * [**AI Overviews**](https://carl-wuerfel.de/en/glossary/ai-overviews) * [**Information Gain**](https://carl-wuerfel.de/en/glossary/information-gain) * [**RAG: Retrieval-Augmented Generation** Term](https://carl-wuerfel.de/en/glossary/rag) ## Sources 1. [Google: Expanding AI Overviews and introducing AI Mode, The Keyword, March 5, 2025](https://blog.google/products-and-platforms/products/search/ai-mode-search/) 2. [Google: AI Mode in Google Search, updates from Google I/O 2025, The Keyword, May 20, 2025](https://blog.google/products/search/google-search-ai-mode-update/) 3. [Google: AI Mode launches in Germany, Austria and Switzerland, Google Blog Germany, October 8, 2025 (in German)](https://blog.google/intl/de-de/produkte/suchen-entdecken/ki-modus-startet-in-dach/) 4. [Google Search Central: AI features and your website, documentation, 2026](https://developers.google.com/search/docs/appearance/ai-features) 5. [Google Search Central: Optimizing your website for generative AI features on Google Search, guide, updated July 2026](https://developers.google.com/search/docs/fundamentals/ai-optimization-guide) 6. [Google Search Console Help: Generative AI performance report (Search), 2026](https://support.google.com/webmasters/answer/16984139?hl=en) 7. [Google Search Central: A guide to Google Search ranking systems, updated December 2025](https://developers.google.com/search/docs/appearance/ranking-systems-guide) 8. [Press, Zhang, Min, Schmidt, Smith, Lewis: Measuring and Narrowing the Compositionality Gap in Language Models, arXiv, 2022](https://arxiv.org/abs/2210.03350)