Query Fan-Out

A complex question, "best project management tool for a remote 20-person agency on a budget", contains several implicit information needs. Fan-out makes them explicit: the system generates sub-queries (pricing comparisons, remote-collaboration features, agency workflows, tool reviews), runs them concurrently against its index, then feeds the combined results to the model for synthesis.

Google describes this technique openly for AI Mode, and equivalent multi-query retrieval underpins ChatGPT Search, Perplexity's research modes, and most serious RAG systems.

Under fan-out, the queries that actually hit the index are invisible and machine-generated, you cannot rank-track them. A page never competitive for the head term can still be cited because it cleanly answers one sub-question, like "project management pricing for small agencies." Citation opportunities multiply for specific pages and shrink for generic ones.

This favors content clusters: a network of focused pages covering pricing, use cases, comparisons, and integrations offers many fan-out entry points, where a single broad page offers one.

Map the sub-questions inside your buyers' big questions, every constraint, comparison, and "for whom" qualifier, and give each a dedicated, direct-answer section or page. Specificity is the currency: fan-out retrieval is looking for the page that nails the narrow query.

Since the sub-queries are unobservable, measure at the answer level: which prompts produce your citations, and from which URLs. Geonimo's daily tracking ties cited URLs back to tracked prompts, revealing which of your pages are winning fan-out retrieval even though the intermediate queries stay hidden.