"Vespa for AI Search: Serving Hybrid Retrieval at Web‑Scale" Modern AI search systems must do more than retrieve documents quickly—they must blend lexical precision, vector semantics, structured filters, and learned reranking under unforgiving latency budgets. This book is written for experienced search engineers, relevance practitioners, and platform architects who need to build production-grade hybrid retrieval systems on Vespa, not just prototype them. It approaches Vespa as an online serving engine where retrieval and ranking are tightly coupled operational decisions. Readers will learn how to model schemas for sparse, dense, and hybrid workloads; represent embeddings with Vespa tensors; compose lexical and nearest-neighbor retrieval in YQL; and design rank profiles that support phased ranking and ML-powered reranking. The book also examines ANN with HNSW, candidate control with filters and targetHits, and the tradeoffs that determine recall, latency, throughput, and memory efficiency at scale. Throughout, the focus stays on production behavior, cost-aware relevance engineering, and the mechanics of serving AI search reliably. Rather than offering a surface tour, the book develops a rigorous mental model for Vespa’s execution path and then extends it to advanced patterns such as chunking, multi-vector retrieval, and version-sensitive implementation choices. Familiarity with search, ranking, and machine learning concepts is assumed. The result is a technically deep, architecture-centered guide for deploying hybrid retrieval systems that remain fast, explainable, and s