Fan-out queries
Chapter 6
Continued from Prompt bank
Fan-out queries are the sub-queries an AI model generates and researches from one user prompt before it writes a single answer.
Defined in Chapter 6: Campaign Research: The Five Datasets That Decide GEO and AEO Strategy of LLM Mastery: How AI Recommends Brands for LLM Domination.
What are fan-out queries?
Fan-out queries are the sub-queries an AI model generates and researches from one user prompt before it writes a single answer.
Chapter 6 says visibility is decided across the whole fan-out set, which is why a brand ranking for the main search phrase can still be missing from the answer; Chapter 13 measures it as fan-out coverage.
Related terms: Fan-out coverage, The five datasets, Retrieval layer and Sub-categories.
Where does the book define fan-out queries?
Chapter 6, Campaign Research: The Five Datasets That Decide GEO and AEO Strategy, defines it:
Models do not answer your question directly. They fan it out: one user prompt becomes a set of sub-queries the system generates and researches, best providers, pricing, reviews, comparisons, local variants, and the final answer is synthesised across all of them.
Which other chapters use fan-out queries?
Chapter 8: Winning the Retrieval Layer, Chapter 9: Listicle Frameworks for AI Crawlers and Chapter 13: Measuring LLM Domination use fan-out queries too.