AI Engineering
Source: https://www.oreilly.com/library/view/ai-engineering/9781098166304/ ↗
The most product-facing book on this shelf.
Where the others explain how a model works, Huyen assumes you'll call one and asks the harder question for a team shipping software: what do you build around it? Evaluation, prompt engineering, retrieval, finetuning, inference cost — the disciplines that actually decide whether an AI feature is good.
For directing AI product, it maps almost exactly onto the decisions you own — build vs buy vs finetune, how to know it works, where latency and cost hide — from someone who has taught and shipped this at scale.
Central argument
A practitioner's map of the new AI stack: how engineering with foundation models differs from traditional ML, and then the disciplines that decide whether an AI product works — evaluation and benchmarking, prompt engineering, retrieval-augmented generation, finetuning, and inference optimization and cost. It treats the model as a component and concentrates on everything you build around it.
Critique
Because it starts from 'the model is a given', it deliberately says little about training or the internal mechanics — you won't learn how a transformer works here. It is also a fast-moving-field snapshot; the durable value is the framework (how to evaluate, how to choose, how to serve) rather than any specific tool.
Why it matters for product
For a CTO/CPO this is arguably the single most directly useful book on the shelf: it is organized around exactly the decisions you own — build vs buy vs finetune, how to evaluate reliably, where cost and latency come from, when RAG beats finetuning. It turns 'we should use AI' into a set of tractable engineering and product choices.
- Beyond Human-Readable: Rethinking Software Engineering Conventions for the Agentic Development Era — Dmytro Ustynov
- To Copilot and Beyond: 22 AI Systems Developers Want Built — Rudrajit Choudhuri, Christian Bird, Carmen Badea & Anita Sarma
- The Second-System Pit of Failure — Terry Coatta & Craig Smith
- The Two Boundaries: Why Behavioral AI Governance Fails Structurally — Alan L. McCann
- On the Evolution of Program State — P. Vixie
- Hands-On Large Language Models — Jay Alammar & Maarten Grootendorst