Learn how to design, evaluate, and deploy reliable applications powered by foundation models.

In AI Engineering, Chip Huyen presents a practical framework for building real-world applications using readily available foundation models. The book explains how AI engineering differs from traditional machine learning engineering and explores the rapidly evolving tools, models, datasets, and application patterns shaping modern AI development.

Through practical guidance and technical insights, you will learn how to:

• Understand foundation models and the modern AI engineering stack

• Evaluate open-ended AI systems using effective evaluation methods

• Select suitable models for different applications

• Improve model performance through prompt engineering

• Build effective context and retrieval systems

• Fine-tune models for specialized use cases

• Design reliable AI agents and workflows

• Reduce inference costs and application latency

• Monitor AI systems and identify potential failures

• Deploy scalable, production-ready AI applications

This book focuses on long-lasting engineering principles instead of individual tools or APIs that may quickly become outdated. It is designed for AI engineers, machine learning engineers, data scientists, engineering managers, technical product managers, and developers who want to use foundation models to solve real-world problems.