Generative AI with RAG

is a comprehensive, production-grade guide for engineers, architects, and data scientists looking to harness
Retrieval-Augmented Generation (RAG)
to build intelligent, scalable AI systems.
This book explores
how to integrate LLMs with enterprise knowledge, vector databases, and real-world data pipelines
to create accurate, reliable, and contextually grounded AI applications. Readers will learn to design, implement, monitor, and scale RAG systems for practical enterprise, research, and startup environments.
Written in a
hands-on, engineering-driven style
, it blends technical explanations, case studies, actionable frameworks, and best practices. Topics include
prompt engineering, embeddings, semantic search, AI agents, evaluation metrics, security, and compliance
, providing a full roadmap from theory to production.
Each chapter concludes with
practical takeaways, checklists, and guidance on common pitfalls
, making this book a
definitive reference for anyone serious about building knowledge-grounded generative AI systems
.

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