When a single AI engine query expands into multiple sub-queries internally before generating an answer, citing different sources for each sub-query.
Query fan-out is a behavior of modern AI engines (especially Google AI Mode and Perplexity) where a single user prompt is decomposed into multiple sub-queries that are processed in parallel. The final answer synthesizes citations from each sub-query's results. For AEO, this means a single user prompt may need optimization across multiple sub-topics to ensure citation. ChatGPT fan-outs have approximately doubled in average length over the past year per Peec AI's analysis.
Optimizing for the literal user prompt may not be enough. AI engines may break it into 5–10 sub-queries, each pulling from different sources. Your content must be relevant to multiple potential sub-queries to be reliably cited.
User asks Perplexity 'best CRM for B2B SaaS startups.' Perplexity internally fans this out into: (1) 'top CRM software 2026,' (2) 'CRM for B2B SaaS,' (3) 'CRM for startups,' (4) 'CRM pricing for small teams,' (5) 'CRM with HubSpot integration.' Each sub-query pulls citations independently. Brands optimized for the literal prompt only may miss citations from sub-queries.
The terms in this glossary aren't theoretical — they're what Lantern's product calculates and reports every month for B2B SaaS teams. See yours in 7 days. 14-day free trial.
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