Introduction to AI Engineering

AI Engineering: Building Applications with Foundation Models (1st Edition) explains how foundation models enable intelligent products. It shows how modern platforms have lowered the barrier to building AI applications. Today, developers can build AI systems without deep prior experience. In this book, Chip Huyen introduces AI engineering as the practice of building applications using ready-to-use foundation models. Understanding the AI Engineering Discipline

The book begins with a clear overview of AI engineering. It explains the field’s scope, key responsibilities, and core principles. It also shows how AI engineering differs from traditional machine learning engineering. This foundation helps readers understand the role of AI engineers in real projects. The Modern AI Application Stack

Next, the book describes the modern AI stack in detail. It covers data preparation, prompt design, and model selection. It also explains orchestration layers, deployment workflows, and production monitoring. Together, these topics show how teams build and maintain AI systems in practice. Evaluating AI Systems in Real-World Use

As organizations adopt AI at scale, risks increase. Small design flaws can spread quickly. Biased data or weak assumptions can cause serious real-world harm. Because of this, evaluation becomes a central focus in AI engineering. The book emphasizes methods that go beyond simple accuracy metrics. Practical Evaluation and Responsible Deployment

To address these challenges, the book explores practical evaluation strategies. It discusses human evaluation, automated checks, and hybrid approaches. It also highlights AI-as-a-judge techniques. These methods help teams test open-ended models in real applications. The guidance supports reliable and responsible AI deployment.From Experimentation to Production

Overall, the book focuses on practical decision-making and thoughtful system design. It helps readers move from experimentation to stable production systems. AI Engineering: Building Applications with Foundation Models (1st Edition) equips practitioners to build scalable, dependable, and responsible AI products across industries.