Query-Fan-out: How AI Systems Break Down Queries and What It Means for GEO
AI systems such as ChatGPT, Perplexity and Google AI Overviews answer search queries with summarised responses drawn from multiple sources. To generate these answers, they rely on what’s known as query fan-out. But what exactly happens during this process, and why does it determine whether content gets cited or stays invisible?
Query fan-out: the essentials at a glance
- Query fan-out is the process by which an AI system automatically breaks a search query down into several sub-queries.
- The system researches these sub-queries in parallel, then aggregates the results into a single answer.
- AI systems never directly cite 85% of the pages they retrieve, so ranking for multiple related queries is essential, not just one.
- Content needs thematic breadth and semantic depth to appear across multiple sub-queries.
- Diverse topic clusters, FAQ sections and structured data all boost citability.
What is query fan-out?
Query fan-out describes the process that splits a single search query submitted to an AI into several sub-queries. AI systems such as Perplexity, ChatGPT or Google AI Mode use this technique to answer complex questions more comprehensively.
Rather than simply matching exact keywords, the system generates related search queries, synonyms and thematically linked terms. It then merges the results of these sub-queries and presents users with a single, consolidated answer.
Figure 1: Schematic representation of query fan-out, using car insurance as an example
How does query fan-out work technically?
The query fan-out process runs through several stages:
The original query is analysed
The system identifies the search intent and relevant entities (clearly defined objects such as brands, people or places).
Sub-queries are generated
Based on this analysis, the system creates several related search queries.
Queries run in parallel
The system sends all sub-queries simultaneously to various data sources.
The results are aggregated
The system merges the results, removes duplicates and ranks them by relevance.
Output
Users receive a bundled, consolidated answer.
Query fan-out and grounding are therefore two closely interlinked steps within the same retrieval process used by AI search systems: fan-out breaks the query down into several sub-queries, while grounding retrieves genuine search results for each sub-query and anchors the AI’s answer in those sources.
How many sub-queries are generated?
The number varies by system: Google’s AI Mode typically generates between 8 and 12 parallel sub-queries (source: Ekamoira), ChatGPT averages 2.3 to 2.8 – though this can rise considerably for more complex questions (source: Peec AI).
An analysis by Dan Petrovic of 365,920 real fan-out search queries also shows that 71.1% of sub-queries consist of seven or more words. Short, generic terms rarely appear as sub-queries – showing that these systems search with precision across a range of angles on a topic.
Query fan-out in AI Overviews
Google’s AI Overviews make extensive use of query fan-out. When a user asks a complex question, the system generates several synthetic sub-queries in the background – so-called “synthetic queries”.
Google has described this mechanism technically in its Thematic Search patent: the system groups search results into thematic clusters and ranks these by their “prominence” – that is, by how many different documents mention a given topic. The more sources that cover a topic, the greater its weighting in the final answer.
You can analyse the sub-queries that ChatGPT generates. To do this, open the browser’s developer console (right-click → Inspect → “Network” tab), trigger a chat response, and search the network responses for “search_model_queries”. This reveals which sub-queries the system submits, letting you target content accordingly.
Perplexity even displays sub-queries directly in its interface – much like Google’s “People also ask”, which you can also use for content creation.
- ChatGPT Query Fan-out Tool (Chrome Extension) – displays sub-queries in real time, with CSV export.
- SE Ranking Fan-out Query Extractor – a free web tool for up to 10 prompts.
- Otterly.ai – analyses fan-outs and tracks AI visibility. Pure query fan-out functionality is free to use.
- Qforia – generates synthetic sub-queries. It requires a Gemini API key but remains free to use at a basic level.
The workflow: Enter relevant prompts, export the generated sub-queries, compare them against your own content, and close any gaps you find.
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