Search has changed. Traditional keyword systems struggle to understand meaning, while modern AI systems often generate answers without reliable sources. The real breakthrough lies in combining the strengths of both: systems that can retrieve the right knowledge and use it to produce trustworthy answers . This book is a practical engineering guide to building those systems. Mastering Semantic Search Engineering walks you step-by-step through the design and implementation of a modern semantic retrieval platform—from raw documents to production-ready, retrieval-augmented AI applications. Instead of focusing only on theory or high-level concepts, the book shows how real retrieval systems are built: how documents are structured, how embeddings power meaning-based search, how hybrid retrieval improves accuracy, and how re-ranking models refine results. You will learn how to construct a complete pipeline that ingests documents, converts them into embeddings, indexes them efficiently, processes user queries intelligently, and retrieves the most relevant knowledge at scale. The journey continues beyond search. The book demonstrates how to transform a retrieval engine into a grounded question-answering system , enabling language models to generate answers that are supported by real documents and traceable evidence. Along the way, you will explore techniques for evaluation, debugging retrieval failures, optimizing ranking quality, and preparing the system for real-world deployment. Inside the book, you will learn how to: • Design document ingestion and chunking pipelines for reliable retrieval • Generate and manage embeddings for large knowledge corpora • Build vector indexes that support fast similarity search • Implement hybrid retrieval combining semantic and keyword signals • Improve search quality with re-ranking models • Diagnose weak matches and retrieval failures • Build Retrieval-Augmented Generation systems that produce grounded answers • Add citations and evidence to increase trust in generated responses • Evaluate retrieval quality using practical benchmark workflows • Deploy and operate semantic search systems in production environments Whether you are building AI assistants, enterprise knowledge systems, developer search tools, or research platforms, this book gives you the practical foundations needed to engineer reliable retrieval systems. If you want to move beyond basic search and learn how modern AI systems actually retrieve and reason over knowledge , this guide will show you how to build them from the ground up.