AI Engineering: Building Applications with Foundation Models

AI Engineering by Chip Huyen is the definitive practitioner's guide for anyone building applications on top of large language models and multimodal foundation models. Written by a Stanford AI lecturer and veteran ML engineer, the book systematically addresses every stage of the development lifecycle: understanding how foundation models work under the hood, establishing rigorous evaluation pipelines, crafting effective prompts and retrieval-augmented generation systems, deciding when and how to finetune, optimizing inference for cost and latency, and assembling a production-grade architecture with guardrails, caching, and user feedback loops. Unlike tutorials tied to specific tools, Huyen focuses on durable fundamentals—why techniques work, when to use them, and how to reason about trade-offs—making it equally valuable for engineers just starting out and those scaling mature AI products.

What it argues

A causal model describing how foundation model design choices and engineering adaptation levers—training data quality, model architecture, post-training alignment, prompt engineering, context construction (RAG), finetuning technique, and inference optimization—operate through intermediate psychological and behavioral states (developer confidence, evaluation reliability, output quality perception) to produce application-level and business outcomes (production reliability, cost-efficiency, user satisfaction, data flywheel growth).