Practical Embedding-Based Retrieval: A Hands-On Guide from Vectors to RAG Pipelines for Smarter AI Applications

Practical Embedding-Based Retrieval: A Hands-On Guide From Vectors to RAG Pipelines for Smarter AI Applications How do you make large language models truly reliable? How do you build AI systems that can find the right information, stay accurate, and adapt to real-world challenges? The answer lies in embedding-based retrieval-the backbone of modern Retrieval-Augmented Generation (RAG) pipelines. This book is a comprehensive, hands-on guide for developers, data scientists, and engineers who want to go beyond theory and build production-ready AI systems. You'll learn how to transform raw data into powerful vector representations, store and query them efficiently with vector databases, and integrate retrieval with generative models to create intelligent applications that actually understand context. Whether you're working on chatbots, enterprise search, or compliance assistants, this guide shows you how to design pipelines that are fast, scalable, and dependable. What sets this book apart is its structured approach that blends clear explanations with real code examples. You'll start with the fundamentals in chapters like Introduction to Embedding-Based Retrieval and Fundamentals of Embeddings. From there, you'll work through Vector Databases and Indexing, Building a Simple Retrieval Pipeline, and Introduction to Retrieval-Augmented Generation (RAG). Later chapters, including Frameworks for RAG Applications, Advanced Retrieval Techniques, and Evaluation and Optimization, prepare you for building enterprise-scale solutions. The final chapters on Real-World Applications and Deployment and the Appendix of Tools and Resources ensure you can take these methods into production with confidence. Each chapter is designed to be practical, clear, and directly actionable. You'll not only understand how embeddings, cosine similarity, and hybrid search work, but you'll also learn how to fine-tune models for your domain, optimize pipelines for speed and accuracy, and scale RAG systems in production. Along the way, you'll find personal insights, common pitfalls to avoid, and proven strategies for making retrieval pipelines trustworthy in real-world environments. If you want to build AI applications that go beyond impressive demos-applications that deliver accurate, grounded, and context-aware intelligence-this book is for you. Get your copy today and start building smarter retrieval-augmented AI systems that your users can rely on.