Overview

Engineering Generative AI Systems provides a practical, systems-first guide to designing, building, and operating production-ready Generative AI. Rather than focusing on isolated techniques or model internals alone, this book examines how Generative AI functions within complete architectures, integrating models with data pipelines, retrieval systems, agent orchestration, deployment infrastructure, monitoring, security, and governance. Written for engineers and technical leaders responsible for real-world delivery, this book covers the full lifecycle of Generative AI systems. Readers will learn how to make informed architectural decisions, build robust pipelines for training and adaptation, deploy scalable inference systems, and operate generative AI safely and efficiently in production environments. Topics such as Retrieval-Augmented Generation, tool-augmented agents, evaluation frameworks, performance optimization, cost control, and operational safety are addressed through an engineering lens grounded in practice. Book-III focuses on resources relevant to deploying, operating, and governing generative AI systems in real-world environments. The emphasis is on system behavior, reliability, safety, and long-term ownership rather than model internals alone. It lays out the transition from building generative AI systems to owning them in production, addressing reliability, safety, cost, and long-term evolution.

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