Generative AI Systems Engineering Design, Build, and Deploy Agentic AI Applications with RAG, LangGraph, and LLM Tooling Unlock the future of AI development with Generative AI Systems Engineering, the definitive guide for advanced developers, data scientists, and AI engineers who want to go beyond single-model applications and master the design of agentic, production-ready AI systems. This book takes you on a deep dive into the principles, architectures, and implementation strategies behind cutting-edge generative AI workflows. You’ll learn how to leverage retrieval-augmented generation (RAG), orchestrate multiple LLMs using LangGraph, and build complex AI agents that interact with real-world data, APIs, and enterprise systems. Inside, you’ll discover: How to architect scalable, reliable, and maintainable AI systems Hands-on Python implementations with LangGraph and LangChain Strategies for debugging, testing, and optimizing AI pipelines Security, compliance, and ethical best practices for responsible AI Real-world projects and mini-case studies designed to mirror production environments Written by a senior AI engineer and technical author with over 10 years of experience building advanced machine learning and agentic AI systems, this book balances rigorous theory with practical, deployable solutions. Whether you are designing enterprise AI agents, knowledge-intensive systems, or multi-model workflows, this guide provides the frameworks, code, and insights to build next-generation AI applications confidently. Take your AI engineering skills to the next level—design smarter, deploy faster, and engineer agentic AI systems with confidence.
