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What does it actually take to ship AI features that don’t fall apart in the real world? In AI Engineering (2024), Chip Huyen maps the new discipline of building products on top of foundation models—where the hard part isn’t training a model from scratch, it’s adapting, evaluating, and operating powerful (and unpredictable) systems at scale. You’ll get a practical tour of the modern AI stack and the “build vs buy” decisions that matter, plus the techniques teams rely on today: prompting, RAG, fine-tuning, and agents. Huyen also tackles the messy realities—hallucinations, inconsistency, prompt attacks, and why evaluation is so tricky for open-ended outputs (including AI-as-a-judge). If you’re building with LLMs, this summary will help you avoid costly dead ends and design AI that’s actually reliable, measurable, and defensible.
