AI Engineering: Building Applications with Foundation Models

What makes this book a must-read?

This book addresses the massive gap between playing with AI demos and shipping production applications. Chip Huyen brings her extensive experience building ML systems at companies like Netflix and Snorkel AI to teach you the engineering practices that actually matter when working with foundation models. You’ll learn prompt engineering that goes beyond basic examples, retrieval-augmented generation (RAG) for grounding models in your data, fine-tuning strategies for customizing models to your needs, evaluation frameworks for assessing model performance, cost optimization techniques for managing API expenses, and deployment patterns for reliable production systems. Unlike academic texts focused on training models, this book assumes you’re using pre-trained foundation models and shows you how to build applications on top of them—which is what most practitioners actually do.

What I will gain?

You’ll develop the skills to build and deploy AI-powered applications professionally. Specifically, you’ll learn to design effective prompts and prompt chains for complex tasks, implement RAG systems that combine retrieval with generation, evaluate and monitor LLM applications in production, optimize costs while maintaining performance, handle edge cases and failure modes gracefully, fine-tune models when prompting isn’t enough, build agents and multi-step reasoning systems, and navigate the rapidly evolving ecosystem of tools and frameworks. Beyond the technical skills, you’ll gain the judgment to know when foundation models are the right solution, how to set realistic expectations with stakeholders, and how to build AI applications that are reliable, cost-effective, and maintainable.

How reading supports online learning?

Online courses on LLMs and AI often focus narrowly on API calls and basic prompt engineering, skipping the hard engineering problems you’ll face in production. This book fills those gaps comprehensively. When your course shows you how to call the OpenAI API, Huyen explains how to build robust systems around those calls—handling rate limits, implementing fallbacks, managing costs, and ensuring quality. The book provides the systems thinking and production engineering context that video tutorials rarely cover. It’s also incredibly current, addressing the latest developments in a fast-moving field. Use it to complement project-based courses: while you’re building your chatbot or AI assistant, the book will guide you through the engineering decisions that separate toy demos from production applications.

Honest Opinion

This is an essential book for anyone serious about building with AI in 2024 and beyond, but it’s not for absolute beginners. You should already understand basic machine learning concepts and have some programming experience. That said, Huyen’s writing is clear, practical, and opinionated in the best way—she tells you what works based on real experience, not just theory. The book is packed with concrete examples, code snippets, and architectural diagrams that make abstract concepts tangible. What makes it particularly valuable is its focus on the unglamorous but critical aspects: evaluation, monitoring, cost management, and reliability. These are the topics that determine whether your AI project succeeds or fails in production, yet they’re rarely covered well elsewhere. The field moves incredibly fast, so some specific tools and APIs will evolve, but the engineering principles and patterns Huyen teaches are fundamental and will remain relevant. If you’re building AI applications professionally or want to move beyond hobbyist projects, this book will save you months of trial and error. It’s the guide to AI engineering that the industry desperately needed.