AI Engineering
Building Applications with Foundation Models


Foundation models have enabled many new AI use cases while lowering the barriers to entry for building AI products. This has transformed AI from an esoteric discipline into a powerful development tool that anyone can use—including thos with no prior AI experience.

In this accessible guide, author Chip Huyen discusses AI engineering: the process of building applications with readily available foundation models. AI application developers will discover how to navigate the AI landscape, including models, datasets, evaluation benchmarks, and the seemingly infinite number of application patterns. The book also introduced a practical framework for developing an AI application and efficiently deploying it.

Understand what AI engineering is and how it differs from traditional machine learning engineering
Learn the process for developing an AI application, the challenges at each step, and approaches to address them
Explore various model adaptation techniques, including prompt engineering, RAG, fine-tuning, agents, and dataset engineering, and understand how and why they work
Examine the bottlenecks for latency and cost when serving foundation models and learn how to overcome them
Choose the right model, dataset, evaluation benchmarks, and metrics for your needs


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