Pages
532
Year
2025
Level
intermediate
Read time
14h
Chip Huyen · O'Reilly Media · 2025
Reviewed by Ashish Sheth · Updated April 2026
AI Engineering
Building Applications with Foundation Models
4.4 / 5
AMAZON · 899 RATINGS
ai engineering · llm
SUBJECTS
What you'll come away with
01.
How to build production applications on top of foundation models
02.
When to use RAG vs fine-tuning vs prompt engineering
03.
Evaluation strategies for AI systems where correctness is fuzzy
04.
Inference optimization techniques for cost and latency
05.
Data engineering patterns specific to AI applications
06.
How to think about AI application architecture end-to-end
Strengths
+Clear, accessible explanations of complex AI/ML concepts
+Practical and implementation-focused rather than theoretical
+Well-researched with extensive references to current literature
+Excellent for software engineers transitioning into AI development
Caveats
−Inconsistent depth: some topics feel surface-level for experienced practitioners
−Limited practical code examples
−Breadth-first approach means some topics lack deep coverage
★ 4.4 FROM 899 READERS ON AMAZON
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Read this if
→Software engineers building their first AI-powered products
→ML engineers wanting a structured view of the AI engineering landscape
→Tech leads evaluating foundation model strategies for their teams
Skip this if
—Researchers focused on model architecture and training
—Complete beginners with no programming experience
—People looking for step-by-step code tutorials
Head-to-head comparisons
AI Engineering vs Designing Machine Learning Systems → AI Engineering vs LLM Engineer's Handbook → AI Engineering vs Building LLMs for Production → AI Engineering vs Build a Large Language Model (From Scratch) → AI Engineering vs Hands-On Large Language Models → AI Engineering vs Prompt Engineering for LLMs → AI Engineering vs Prompt Engineering for Generative AI → AI Engineering vs Co-Intelligence → AI Engineering vs Building Agentic AI Systems → AI Engineering vs AI Agents in Action → AI Engineering vs The Coming Wave → Frequently asked
Is AI Engineering good for beginners?
You need some software engineering experience. It's not a learn-to-code book. But you don't need a PhD in ML either. If you can write Python and understand APIs, you'll follow along.
How is AI Engineering different from Designing Machine Learning Systems?
Designing ML Systems covers the full ML lifecycle (data, training, serving). AI Engineering focuses specifically on building applications with foundation models like GPT and Claude. Think of it as the sequel for the LLM era.
Is AI Engineering still relevant given how fast AI moves?
The book focuses on principles and patterns, not specific tools. RAG, evaluation, and inference optimization aren't going away. The fundamentals hold even as models change.
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