Query Fanout — How AI Searches The Web
Understanding how AI expands your questions into multiple sub-queries
Query Fanout
How Each Platform Handles Query Fanout
The CITE Framework™ for Query Fanout
Capture AI sub-queries using our proprietary methodology:
Frequently Asked Questions
What is Query Fanout?
Query Fanout is an information retrieval technique where AI systems expand a single user query into multiple sub-queries to capture different intents and retrieve diverse results. As Google's Elizabeth Reid explained at I/O 2025: "Search recognizes when a question needs advanced reasoning, calls on Gemini to break the question into subtopics, and issues a multitude of queries simultaneously." For example, "best project management tool" triggers 2-11 sub-queries about features, pricing, reviews, comparisons, and integrations.
How many sub-queries does each AI platform run?
The number varies by platform and query complexity. ChatGPT typically runs 2-5 sub-queries via Bing, prioritising domain trust and readability. Perplexity runs 3-8 sub-queries with a preference for academic sources, transparently showing users which searches it performs. Google AI Mode can run up to 11 sub-queries for complex questions, using custom Gemini to break questions into subtopics. For complex questions, some platforms run dozens of searches simultaneously.
How does Query Fanout affect my content strategy?
Query Fanout means creating content that answers related questions, not just the main topic. According to seoClarity research, 97% of AI Overviews cite at least one source from the top 20 organic results. If you write about "best CRM software," also cover pricing comparisons, feature breakdowns, implementation guides, and reviews. Structure content with clear H2 headers for each subtopic so AI can extract specific passages for different sub-queries.
What sources get prioritised during Query Fanout?
Different platforms weight sources differently. ChatGPT favours domain rating and content readability (Flesch Score). Perplexity has a strong academic source preference, pulling from research papers and authoritative publications. Google AI Mode draws heavily from top-ranking organic pages. Reddit content appears in 46.7% of AI citations. News sources get weighted for freshness-dependent queries. The more authoritative sources mentioning your brand, the more likely you are to be cited.
How can I optimise my content for Query Fanout?
Create comprehensive content clusters that anticipate sub-queries. Use clear H2 headers for each angle of the topic. Include FAQ sections with direct 40-60 word answers. Add comparison tables, pricing breakdowns, and step-by-step guides. Use semantic URLs (5-7 words describing content) which get 11.4% more citations. Structure content so AI can extract specific passages for each sub-query. Build topical authority by covering subjects thoroughly across multiple interlinked pages.
Why is Perplexity more transparent about Query Fanout?
Perplexity openly shows the sub-queries it uses and the sources it draws on, helping users understand how answers are formed. You can see exactly which sub-queries Perplexity considers relevant, which eliminates guesswork in your content strategy. Tools like Keyword Surfer now reveal ChatGPT's fan-out queries, but Perplexity has been transparent from the start. This visibility makes Perplexity valuable for content strategists researching what sub-topics AI considers relevant.
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