Debugging & Optimizing RAG Pipelines: Comprehensive step-by-step Guide to Unlocking Rapid Debugging and Performance Gains in RAG

By Brian Pitman

Unlock the full potential of modern artificial intelligence with "Debugging and Optimizing RAG Pipelines: A Comprehensive Guide to Retrieval-Augmented Generation, LLMs, and MLOps Architecture." This must-have resource is your definitive guide to mastering machine learning pipelines and large language models using Python. Whether you're an AI developer, data scientist, or machine learning enthusiast, this book provides clear, practical insights into debugging machine learning models, building scalable MLOps architectures with RAG pipelines, and orchestrating complex multi-agent AI systems.

Inside, you'll discover step-by-step instructions and real-world code examples that cover everything from LLM transformer techniques and prompt programming to advanced topics like multimodal retrieval augmented generation. Beginners will benefit from an accessible introduction to crewai langgraph for visualizing complex graph-based workflows, while seasoned professionals will appreciate deep dives into fine-tuning strategies, load testing, and ethical considerations for responsible AI development.

This book not only demystifies debugging and optimizing machine learning models but also serves as the ultimate guide to Retrieval Augmented Generation (RAG) and RAG LLM systems. Enhance your expertise in LLM programming agents, implement state-of-the-art retrieval mechanisms, and harness the power of generative AI to build next-generation intelligent systems.

Perfect for anyone interested in LLMs, generative AI books, or a comprehensive guide to retrieval augmented generation, this book is packed with actionable takeaways designed to boost your productivity and innovation in the AI space. Get ready to transform the way you build and deploy advanced AI solutions-your journey to mastering RAG pipelines starts here

• Format: Paperback
• Language: English
• ISBN: B0DT1HC2D4
• ISBN13: 9798306974583
• Release Date: January 2025
• Publisher: Independently Published
• Length: 160 Pages
• Weight: 0.64 lbs.
• Dimensions: 0.3" x 7.0" x 10.0"