about this book
Building Reliable AI Systems is a practical guide to closing the gap between an AI prototype that impresses and a production system you can trust. It focuses on the nonfunctional requirements of large language model (LLM) applications: accuracy, grounding, safety, fairness, privacy, evaluation, and operability. Rather than surveying model architectures in the abstract, it walks through the engineering techniques that make real systems dependable, using working projects to anchor every idea.
Who should read this book
This book is written for software engineers, machine-learning engineers, AI engineers, and technical leaders who are building or preparing to build LLM-powered applications and agents for production. If you’ve written a prompt, called a model API, or wired up an agentic workflow and felt the distance between a promising demo and an AI system you’d put in front of real users, you are the intended reader.
To follow the examples, you should be comfortable reading Python and have a basic familiarity with machine learning and NLP concepts. You don’t need a research background. Every reliability technique is introduced from the problem it solves, so you can start applying the material without mastering the underlying theory.