Debugging and Optimizing RAG Pipelines: A Practical Guide for AI Developers
This hands-on guide explores the challenges and solutions of building high-performance Retrieval-Augmented Generation (RAG) pipelines. As AI-powered applications become more complex, understanding how to monitor, debug, and fine-tune RAG systems is essential. This book provides a clear and practical roadmap for developers working with large language models, search engines, and generation components to ensure reliability, accuracy, and efficiency in production-grade AI systems.
From real-time monitoring to error tracing and optimization techniques, this book walks you through every stage of a RAG pipeline. Whether you're troubleshooting hallucinations, improving retrieval quality, or scaling a system for enterprise use, you'll find actionable guidance and ready-to-use code examples that save time and reduce friction.
Debugging and Optimizing RAG Pipelines goes beyond just theory. It addresses real-world challenges that AI developers face when building and deploying RAG systems. You'll learn to identify issues early, implement observability tools, reduce latency, eliminate hallucinations, and continuously improve system performance. Each chapter includes practical tips, hands-on examples, and expert insights designed to help you create RAG pipelines that are robust, scalable, and easy to maintain.Key Features of This Book
Real-world debugging workflows for complex RAG systems
Best practices for prompt design, logging, and feedback loops
Performance tuning tips to optimize latency and generation quality
Practical tools: Prometheus, Grafana, LangSmith, and more
Drift detection, caching strategies, and security implementation
Fully documented code examples for step-by-step learning
Insights into the future of multimodal and agentic RAG systems
This book is perfect for AI developers, machine learning engineers, and data scientists building LLM-based applications. If you've already worked with RAG or LLM pipelines and want to push them to production-ready quality, this guide is your go-to resource. It's also ideal for backend engineers integrating AI models into microservices and product managers overseeing intelligent features.
If you're ready to move from experimental to enterprise-grade AI systems, Debugging and Optimizing RAG Pipelines gives you the tools and confidence to do just that. Get your copy now and take control of your RAG pipeline's performance, reliability, and accuracy-because building smarter AI starts with better engineering.外文書商品之書封，為出版社提供之樣本。實際出貨商品，以出版社所提供之現有版本為主。部份書籍，因出版社供應狀況特殊，匯率將依實際狀況做調整。
無庫存之商品，在您完成訂單程序之後，將以空運的方式為你下單調貨。為了縮短等待的時間，建議您將外文書與其他商品分開下單，以獲得最快的取貨速度，平均調貨時間為1~2個月。
為了保護您的權益，「三民網路書店」提供會員七日商品鑑賞期(收到商品為起始日)。
若要辦理退貨，請在商品鑑賞期內寄回，且商品必須是全新狀態與完整包裝(商品、附件、發票、隨貨贈品等)否則恕不接受退貨。
