AI Engineering (Book)
A technical book for engineers building production LLM applications — from Python and ML foundations through RAG, agents, deployment, and Staff+ system design. Authored in Quarto, hosted at book.jameshu.io.
What This Is
A technical book I wrote for software engineers who want to build production AI systems — AI Engineering: Building Production-Ready LLM Applications. It starts from fundamentals (Python patterns for AI, ML basics, your first LLM app) and builds up to Staff+ concerns like multi-region deployment, cost optimization, and cross-team technical leadership.
Read it free online → book.jameshu.io
What's Inside
Five parts, 32 chapters, 16 appendices, and a code reference — each chapter with runnable examples.
- •Part I — Foundations: the AI engineering landscape, Python for AI, ML fundamentals, your first LLM application.
- •Part II — Core LLM Development: transformers & NLP, prompt engineering, RAG systems, agentic systems.
- •Part III — Production Engineering: deployment & inference infrastructure, orchestration, observability & guardrails, multi-cloud, MLOps & evaluation, security & adversarial robustness, multimodal, responsible AI.
- •Part IV — Professional Growth: deepening expertise, project ownership, technical communication, mentorship.
- •Part V — Staff+ Engineering: system design at scale, technical decision-making, performance, research-to-production, cross-team leadership, data architecture, reliability, cost engineering.
How It's Built
Authored in Markdown and rendered with Quarto to a searchable HTML site and a PDF. Diagrams are written as code in D2 (180+ diagrams) and embedded as SVG. Code examples are real, runnable Python. The site is hosted at book.jameshu.io — rendered by Quarto in Vercel's build and deployed on every push.
Who It's For
Software engineers moving into AI/ML, backend and full-stack developers integrating LLMs, and tech leads architecting AI systems. Assumes basic Python; assumes no prior AI/ML experience.