Analyze How AI Platforms Decompose Queries into Sub-Queries

Query fan-out is the process by which AI platforms like ChatGPT, Perplexity, Claude, and Gemini decompose complex user queries into multiple sub-queries. For example, a query like 'Best CRM for small businesses' might be broken down into sub-queries about pricing, integrations, features, and reviews.

It varies with the prompt. A simple question may trigger one or two searches, while a broad comparison can trigger many more. The Query Fanouts tab shows the actual sub-queries behind each of your tracked prompts and how often each one came up.

Understanding query fan-out helps you optimize your content to match how AI platforms break down queries. By covering all sub-queries in your content, you increase the chances of being cited in AI-generated answers and improve your visibility in AI search results.

Yes. Each engine expands a prompt in its own way, and only some engines expose those sub-queries. Orchly surfaces fan-out mainly from ChatGPT and Perplexity today, and the Models column shows which engine produced each sub-query.

By analyzing query fan-out patterns, you can identify which sub-queries are commonly generated for topics in your industry. Create comprehensive content that addresses all sub-queries, increasing your chances of being cited in AI-generated answers.

Topic coverage measures how well your content addresses all the sub-queries generated from a complex query. Higher coverage means your content is more likely to be cited in AI answers. Our tool shows you your coverage percentage and identifies gaps.

Fan-out comes from the prompts you track. Add prompts your competitors win, such as comparison or "best X for Y" questions, and their sub-queries appear in the Query Fanouts tab with your visibility on each one.

Daily. Sub-queries are collected each time your tracked prompts run, so the list grows as new questions come up. Sort by Times Used to see the ones engines ask most.