AI Engineering: Building Applications with Foundation Models
The definitive practitioner's guide to building production AI applications with LLMs.
What it covers
Chip Huyen covers the full stack: model selection, prompt engineering, RAG architectures, evaluation, deployment, monitoring, and the organizational patterns that make AI projects succeed.
Why it matters
There's a massive gap between 'I can call the OpenAI API' and 'I can build reliable AI systems at scale.' This book bridges that gap with real engineering rigor.
What it made me think
The evaluation chapter changed my approach to AI system design. You can't improve what you can't measure, and most teams are flying blind on AI quality.
The short version
This is the O'Reilly book that should be on every AI engineer's desk in 2026
My take
Even for people who have shipped production AI systems for years, this book has new patterns in it. The evaluation framework alone is worth the price.
How it connects
- production AI architecture
- LLM evaluation frameworks
- MLOps and AI engineering maturity
Bottom line
Stop prototyping, start engineering. This book shows you how.
Takeaways
- Foundation models are a new compute primitive — treat them like infrastructure, not magic
- RAG architecture patterns that actually work in production (and the ones that don't)
- Evaluation is the most underinvested area of AI engineering — systematic approaches exist
- Prompt engineering is engineering — version it, test it, monitor it like code
- The best AI applications are built by teams that iterate fast with real user feedback
Brendon Score: 9.5/10
- Relevance: 10.0/10 — +2.50
- Depth: 10.0/10 — +2.50
- Actionability: 10.0/10 — +2.50
- Freshness: 8.0/10 — +2.00
- Total (average): 9.5/10
Why this is here
Checks cleared: owner-pinned.