This episode is part of the AI Summary series covering the AI Search Manual chapter by chapter. Chapter 14 explores how attribution works in a generative search environment where the visible query is only the surface and the real retrieval happens behind the scenes.

The discussion looks at query fan-out, where a simple user prompt splinters into dozens of synthetic subqueries targeting entities, attributes, and data sources. We cover techniques like query perturbation testing and co-citation analysis that help reverse engineer this process, exposing which content consistently surfaces and why.

We also dive into the role of entities as the anchors of retrieval, explaining how entity mapping and query-entity attribution matrices create a clearer picture of eligibility. The episode highlights how merging these maps into a single dataset gives marketers a live control panel for understanding and shaping where their content appears in AI search.

Read the full chapter at ipullrank.com/ai-search-manual