Building Reliable AI Systems

Get the eBook free when you register your print book at Manning. This book shows you exactly how to guide large language models from research prototypes to scalable, robust, and efficient production systems. From model training to maintenance, an engineer will find everything they need to work with LLMs in this one-stop guide. This book complements Sebastian Raschka’s Build a Large Language Model (From Scratch), which takes a hands-on, ground-up approach to constructing LLMs. While Raschka’s book focuses on building models from scratch, this book centers on deploying, optimizing, and maintaining reliable, production-grade AI systems. Inside Building Reliable AI Systems you’ll learn how to: • Deploy LLMs into production • Detect and reduce hallucinations • Mitigate bias • Optimize LLM performance and resource usage • Advanced prompt engineering techniques • Build intelligent agents and Retrieval-Augmented Generation Building Reliable AI Systems is a guide to putting LLMs into production in the real world. The book bridges the gap between theory and practice. You’ll go beyond basics like prompting into advanced optimizations: intelligent agents, Retrieval Augmented Generation (RAG), and in-depth solutions for mitigating hallucinations and bias. About the book Building Reliable AI Systems is a comprehensive guide to creating LLM-based apps that are faster and more accurate. It takes you from training to production and beyond into the ongoing maintenance of an LLM. In each chapter, you’ll find in-depth code samples and hands-on projects—including building a RAG-powered chatbot and an agent created with LangChain. Deploying an LLM can be costly, so you’ll love the performance optimization techniques—prompt optimization, model compression, and quantization—that make your LLMs quicker and more efficient. Throughout, real-world case studies from e-commerce, healthcare, and legal work give concrete examples of how businesses have solved some of LLMs common problems. About the reader For data scientists or software engineers confident in Python and NLP. About the author Rush Shahani is a seasoned AI Engineer and CTO of Persana AI, a YCombinator-backed startup. At Persana, he leads the development of natural language processing and large language model systems that provide actionable insights to companies in order to drive revenue growth. His experience includes building AI systems at companies like LinkedIn, Element AI, and Shopify.