RETRIEVAL AUGMENTED GENERATION IN PRODUCTION

This book is a practical, end-to-end guide to building product-implementation-ready Retrieval-Augmented Generation (RAG) systems for high-stakes domains such as healthcare, finance, education, legal services, and customer support. While Artificial Intelligence (AI) advances rapidly, Large Language Models (LLMs) continue to face challenges with factual consistency and domain-specific accuracy. LLM-based RAG systems address these limitations by integrating live, external knowledge sources to produce grounded, current, and trustworthy outputs.

Readers are taken through the full RAG pipeline with MLOps/LLMOps — while tackling 30+ real-world implementation challenges, including data parsing & chunking, prompt rephrasing, retrieval quality, response synthesis, hallucination mitigation, evaluation frameworks, serving & monitoring enhancement, orchestration optimization, and graph-, tabular-, and agentic-RAG patterns. Clear architectures, case studies, and runnable code illustrate how to design, implement, validate, monitor, and scale robust RAG systems.

The book also provides a balanced perspective on the current limitations of RAG approaches and their future potential as part of emerging agentic AI ecosystems. Whether you are an engineer, product leader, or researcher, this book equips you to deliver reliable, business-ready AI solutions while staying ahead of rapidly evolving technologies.

All supplementary material (i.e. sample codes) are available at https://github.com/junxu-ai/RAG_Codes.

Contents:

• Preface
• About the Author
• Introduction
• MLOps, LLMOps and RAGOps for Production
• RAG Challenges and Solutions
• Data Processing
• Embedding and Vector Database
• Query Transformation and Prompt Engineering
• Retrieval Techniques
• Augmentation and Refinement Techniques
• Generation Techniques
• Evaluation Methodology
• Serving and Monitoring
• Pipeline and Orchestration
• RAG with Database and Text2SQL
• RAG with Knowledge Graph
• Agentic RAG
• Conclusion
• Index

Readership: A guidebook for AI/ML and data engineers, solution architects, and other industrial professionals seeking production-ready RAG systems and actionable roadmaps for designing, evaluating, and operating GenAI solutions; Also a textbook for graduate students in the field of data or computer engineering.