This episode is part of the AI Summary series covering the AI Search Manual chapter by chapter. Chapter 8 explains query fan-out, latent intent, and source aggregation — the mechanics that turn a single user query into dozens of sub-queries driving generative answers.

We explore how systems expand an input into related intents, identify explicit and implicit slots, generate rewrites, and anticipate follow-up questions. The episode shows how routing directs these sub-queries to different sources and modalities, from web indexes and APIs to video transcripts and structured data.

We then break down the selection funnel, where retrieved chunks are filtered by extractability, evidence density, scope clarity, authority, freshness, and safety before reaching synthesis. High-quality content often gets excluded if it fails on structure or format, which highlights why chunk-level engineering matters as much as page-level optimization.

The strategic takeaway is clear: winning in GEO requires intent coverage across the fan-out, multi-modal parity so content fits the system’s preferred formats, and chunk-level readiness for synthesis. Measurement also changes, shifting from keyword rankings to sub-query recall, evidence density, and citation stability.

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