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This book focuses on long-lasting engineering principles instead of individual tools or APIs that may quickly become outdated.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.