Production-Grade GenAI Engineering: Designing, Operating, and Scaling Generative AI Systems in Production

Generative AI systems fail differently in production.They rarely crash outright. Instead, they degrade quietly—costs spike without traffic growth, quality drifts without deploys, and control is lost when it matters most.Production-Grade GenAI Engineering is a practical, system-level guide for engineers building and operating generative AI systems in real production environments. This book focuses not on model hype or prompt tinkering, but on control, reliability, cost containment, and operational discipline at scale.Inside this book, you will learn how to:- design production-ready GenAI platforms with clear architectural and control boundaries- enforce budgets, guardrails, and degradation paths at runtime- scale inference safely without amplifying cost, latency, or operational fragility- detect and manage drift, quality regressions, and vendor instability- improve observability, feedback loops, and operational visibility across evolving AI systems- define ownership between platform, application, and AI engineering teams- respond to incidents using production playbooks and first-5-minute actionsThis book is written for senior engineers, platform teams, SREs, architects, and technical leaders responsible for systems that must remain predictable under pressure.It is not an introduction to AI fundamentals. It assumes you are already working beyond prototypes and need systems that stay measurable, governable, and reliable in production.If you are building generative AI systems that cannot fail loudly—but must never fail silently—this book provides the structure, patterns, and operational mindset required to keep control in production. Read more