AI Engineering

Chip Huyen has built a reputation for writing about machine learning systems with unusual clarity and technical depth, and AI Engineering continues that pattern applied to the foundation model era. The book covers the full practical stack of building applications on top of large language models and similar systems — prompting strategies, fine-tuning, evaluation, retrieval-augmented generation, and the infrastructure decisions that determine whether an AI application actually performs reliably at scale. At 532 pages, it doesn’t shy away from technical detail, and that’s precisely its value: this is written for engineers who need to understand tradeoffs, not just vocabulary. Huyen draws on substantial industry experience building production ML systems, and that grounding shows throughout in the book’s emphasis on evaluation rigor and the unglamorous engineering work that separates a working application from an impressive demo. For technical practitioners who want a serious, well-structured reference on building with foundation models rather than another conceptual overview, this has become one of the field’s go-to resources.

Author: Chip Huyen.