"Weaviate for RAG: Schema-Aware Vector Search with Hybrid Queries"
Built for experienced engineers, search specialists, and AI architects, this book treats retrieval-augmented generation as a systems problem rather than a prompt-engineering trick. It shows how Weaviate becomes most powerful when retrieval is designed deliberately: schema, vector spaces, lexical indexes, filters, rerankers, and generative workflows all shaping answer quality. Readers who already know the basics of embeddings and LLM applications will find a rigorous guide to building RAG that is precise, explainable, and production-ready.
Across the book, you will learn how to design collections for high-fidelity retrieval, model properties for both semantic and keyword access, and use named vectors to partition retrieval intent. The text goes deep on BM25F, filtering, metadata-aware recall, hybrid fusion, weighting strategies, reranking, and prompt context assembly. Just as importantly, it teaches how to diagnose failure modes, evaluate retrieval quality, tune multi-stage pipelines, and adapt architectures as corpus shape, query mix, and operational requirements change.
A distinguishing feature of this book is its version-aware, engineering-first perspective. It addresses practical consequences of Weaviate milestones such as hybrid fusion defaults and named-vector support, helping teams migrate safely without losing relevance quality. The result is a focused, advanced blueprint for building grounded, maintainable RAG systems that continue to perform as data models, ranking strategies, and platform capabi
