In AI Engineering: Building Applications with Foundation Models, Chip Huyen delivers a timely, insightful, and deeply practical guide for the new era of AI development. With the advent of foundation models and the rise of model-as-a-service platforms, the barriers to building AI applications have never been lower—but navigating this landscape requires a clear understanding of the tools, techniques, and trade-offs involved. Huyen’s book provides exactly that.

The book opens with a compelling overview of AI engineering as a discipline distinct from traditional machine learning engineering. Rather than focusing on training models from scratch, AI engineering is about building systems that harness pre-trained foundation models—like GPT, Claude, or Stable Diffusion—to deliver real-world value. Huyen does an excellent job demystifying this new stack and outlining the emerging best practices for developing, evaluating, and deploying AI-powered products.

One of the standout strengths of this book is its balance between breadth and depth. Readers are introduced to a wide range of topics—prompt engineering, retrieval-augmented generation (RAG), fine-tuning, dataset design, latency optimization, cost management, and more—all within a coherent framework for building applications. The discussion on evaluation is especially noteworthy: in a world where models generate open-ended outputs, robust evaluation strategies (including AI-as-a-judge) become essential, and Huyen tackles this challenge head-on.

The book is accessible even to those without deep AI backgrounds, yet it doesn’t shy away from technical depth. Engineers, product managers, and startup founders alike will find actionable guidance, from choosing the right model and dataset to understanding deployment trade-offs.

Chip Huyen brings her considerable industry and academic experience to bear, drawing on insights from her work at NVIDIA, Snorkel AI, and Stanford. Readers of her previous book, Designing Machine Learning Systems, will recognize the same clarity of thought and practical mindset—this new work builds upon that foundation while targeting the unique demands of today’s AI ecosystem.

In summary, AI Engineering is an essential read for anyone looking to build with foundation models. Whether you’re experimenting with LLMs for the first time or architecting large-scale AI applications, this book offers a valuable roadmap for navigating the exciting and rapidly evolving field of AI engineering.

Rating: ★★★★★ (5/5)