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AI Engineering: A Practical Guide to Building AI Applications with Foundation Models

Recent breakthroughs in Artificial Intelligence (AI) have dramatically increased demand for AI-powered products while simultaneously lowering the barriers to entry for developers and businesses. Thanks to the model-as-a-service approach, AI is no longer a niche academic field—it has become a powerful, accessible development tool that anyone can use, even without prior AI experience. AI engineering book

In the book AI Engineering, author Chip Huyen introduces readers to the emerging discipline of AI engineering—the practice of building real-world applications using readily available foundation models. Unlike traditional machine learning engineering, AI engineering focuses on leveraging pre-trained models to rapidly design, evaluate, deploy, and scale AI-driven applications.

The book begins with a clear overview of what AI engineering is, how it differs from classical ML engineering, and how the modern AI technology stack is evolving. As AI systems become more widely used, the risks of model failures, hallucinations, and unintended consequences also increase—making model evaluation more critical than ever. Chip Huyen explores modern evaluation strategies for open-ended AI systems, including the fast-growing AI-as-a-judge approach.

Readers will gain a comprehensive understanding of the AI ecosystem, including AI models, datasets, evaluation benchmarks, application patterns, and deployment strategies. The book provides a structured framework for developing AI applications—starting with simple techniques and advancing toward more sophisticated methods—while emphasizing efficiency, scalability, latency, and cost optimization.

What You’ll Learn from This Book:

Understand AI engineering and how it differs from traditional machine learning engineering

Learn a step-by-step framework for building and deploying AI applications

Explore key model adaptation techniques such as prompt engineering, RAG, fine-tuning, agents, and dataset engineering

Identify performance bottlenecks related to latency and cost when serving foundation models

Choose the right AI models, datasets, evaluation metrics, and benchmarks for your use case

Gain practical insights into deploying scalable and reliable AI systems in production

Chip Huyen currently works at Voltron Data, focusing on accelerating data analytics on GPUs. She has previously worked at Snorkel AI and NVIDIA, founded an AI infrastructure startup, and taught Machine Learning Systems Design at Stanford University. She is also the author of the Amazon bestseller Designing Machine Learning Systems.