Preface

With Large Language Models (LLMs) now powering everything from customer service chatbots to sophisticated code generation systems, generative AI has rapidly transformed from a research lab curiosity to a production workhorse. Yet a significant gap exists between experimental prototypes and production-ready AI applications. According to industry research, while enthusiasm for generative AI is high, over 30% of projects fail to move beyond proof of concept due to reliability issues, evaluation complexity, and integration challenges. The LangChain framework has emerged as an essential bridge across this divide, providing developers with the tools to build robust, scalable, and practical LLM applications.

This book is designed to help you close that gap. It’s your practical guide to building LLM applications that actually work in production environments. We focus on real-world problems that derail most generative AI projects: inconsistent outputs, difficult debugging, fragile tool integrations, and scaling bottlenecks. Through hands-on examples and tested patterns using LangChain, LangGraph, and other tools in the growing generative AI ecosystem, you’ll learn to build systems that your organization can confidently deploy and maintain to solve real problems.