AI Engineering
A structured book on building applications with foundation models, covering evaluation, prompting, retrieval, fine tuning, inference cost and architecture. The reference treatment of a subject that mostly exists as scattered blog posts.
Book
AI & Machine Learning
Chip Huyen
4 to 6 weeks
Intermediate
About $50
What it is
A book that treats building with foundation models as an engineering discipline, working through model selection, evaluation methodology, prompt engineering, retrieval augmented generation, fine tuning, inference optimisation, and the architecture of AI applications end to end.
Why a book helps here
This field's knowledge is scattered across blog posts, conference talks and threads, each covering one piece and assuming the rest. A single coherent treatment that puts evaluation before optimisation and explains why is worth a great deal, particularly to anyone who joined recently and has no map.
The chapter that pays for the book
Evaluation. Most teams building AI features have no reliable way to tell whether a change made things better, which means every decision after the first is guesswork. The book is unusually thorough about this and it is the highest leverage thing most teams are missing.
Best for: engineers building AI products who want the whole picture in one place, from someone who has done it at scale.
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