Retrieval Augmented Generation In Production: Architecture, Patterns, And Runbooks
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A01=Jun Xu
AgentOps
Author_Jun Xu
Best Practices
Category=UYQL
Category=UYQM
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eq_computing
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Fine-tuning
forthcoming
GenAI
Generative AI
Guardrails
Large Language Models
LLMOps
LLMs
MLOps
Productionization
Prompt Engineering
RAG
RAGOps
Retrieval-Augmented Generation
Product details
- ISBN 9789819829323
- Publication Date: 28 Jun 2026
- Publisher: World Scientific Publishing Co Pte Ltd
- Publication City/Country: SG
- Product Form: Paperback
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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.
