AI Engineering: Building Applications with Foundation Models

Chip Huyen · Curated 2026-03-23

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AI Engineering: Building Applications with Foundation Models Chip Huyen

A practitioner's guide to foundation-model applications, covering evaluation, RAG, fine-tuning, and AI system architecture.

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

I would prioritize the evaluation chapters when planning an AI application, then define quality checks around the application's intended use.

The short version

This is the O'Reilly book that should be on every AI engineer's desk in 2026

My take

For engineers who already ship AI applications, I would compare the chapters on evaluation, RAG, fine-tuning, and inference optimization with the problems they need to solve.

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)
• The book covers evaluation methodology and evaluating AI systems; consider these chapters when designing application-specific quality checks.
• 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

As an Amazon Associate I earn from qualifying purchases.