AI Engineering
Recent breakthroughs in AI have not only increased demand for AI products, they've also lowered the barriers to entry for those who want to build AI products. The model-as-a-service approach has transformed AI from an esoteric discipline into a powerful development tool that anyone can use. Everyone, including those with minimal or no prior AI experience, can now leverage AI models to build applications. In this book, author Chip Huyen discusses AI engineering: the process of building applications with readily available foundation models. The book starts with an overview of AI engineering, explaining how it differs from traditional ML engineering and discussing the new AI stack. The more AI is used, the more opportunities there are for catastrophic failures, and therefore, the more important evaluation becomes. This book discusses different approaches to evaluating open-ended models, including the rapidly growing AI-as-a-judge approach. AI application developers will discover how to navigate the AI landscape, including models, datasets, evaluation benchmarks, and the seemingly infinite number of use cases and application patterns. You'll learn a framework for developing an AI application, starting with simple techniques and progressing toward more sophisticated methods, and discover how to efficiently deploy these applications.
Stats for nerds
6recommend
9mentions
9people
2025–2026years
67% recommend33% neutral
Mentions per year
What Hacker News says
- Absolutely; I'd check out AI Engineering by Chip Huyen to get a fundamental understanding of the space and do a couple of side projects in the area to get familiar and be able to speak to the nuances of working with LLMs, and that will be enough to start working to transition and apply for relevant roles.
- This is not a complete solution, but two books you should buy immediately:
Designing Data-Intensive Applications by Martin Kleppmann
AI Engineering by Chip Huyen
- I recently did a deep dive into Chip Huyen's "AI Engineering" and one argument stood out as particularly crucial for teams building with LLMs today. It's the clear, principled distinction between Retrieval-Augmented Generation (RAG) and finetuning.
Many engineering teams instinctively reach for finetuning as a way to "teach" a model their private data. The thinking is that if you train it on your documents, it will "know" them. However, this is often a misuse of the technique. Finetuning is most effective at altering the form and behavior of a model—making it communicate in a certain style, adhere to a specific JSON schema, or follow a complex chain of instructions. It is an expensive and imprecise tool for knowledge injection.
The book argues that RAG is the superior tool for providing facts. By retrieving relevant information from an external knowledge base at inference time and adding it to the prompt context, you get several advantages:
Factual Grounding: The model is less likely to hallucinate because its context is bounded by the retrieved documents.
Traceability: You know exactly which source documents were used to generate an answer.
Up-to-date Knowledge: The knowledge base can be updated continuously without the cost of retraining/finetuning the model itself.
The core takeaway is that teams should default to RAG for knowledge-based tasks and reserve the more complex and expensive process of finetuning for tasks that require altering the model's fundamental behavior. This seems like a critical architectural decision that could save significant resources. Curious to hear how others are approaching this trade-off.
- I believe that most of the papers presented here focus on acquiring knowledge rather than deep understanding. If you’re completely unfamiliar with the subject, I recommend starting with textbooks rather than papers. The latest Bishop’s "Deep Learning: Foundations and Concepts (2024)" [1] is an excellent resource that covers the "basics" of deep learning and is quite updated. Another good option is Chip Huyen’s "AI Engineering (2024)" [2]. Another excellent choice will be "Dive into Deep Learning" [3], Understanding Deep Learning [4], or just read anything from fast.ai and watch Karpathy's lectures on YouTube.
[1]: https://www.bishopbook.com [2]: https://www.oreilly.com/library/view/ai-engineering/97810981... [3]: https://d2l.ai [4]: https://udlbook.github.io/udlbook/
- I haven't read these all the way through myself but I've seen enough of them that I'm confident suggesting them:
- Prompt Engineering for LLMs by John Berryman and Albert Ziegler: https://www.amazon.com/Prompt-Engineering-LLMs-Model-Based-A...
- AI Engineering by Chip Huyen, which I recommend based on the strength of this extract about "agents": https://huyenchip.com/2025/01/07/agents.html
- Some that i am looking into; these are "practical" books which do not focus on the theory/algorithms but given that they are available (library/models/whatever), how to build your apps using them;
1) Designing Machine Learning Systems: An Iterative Process for Production-Ready Applications by Chip Huyen.
2) AI Engineering: Building Applications with Foundation Models by Chip Huyen (this is a very recent book).
3) Generative Deep Learning: Teaching Machines To Paint, Write, Compose, and Play by David Foster.
4) Building LLMs for Production: Enhancing LLM Abilities and Reliability with Prompting, Fine-Tuning, and RAG by Bouchard & Peters.
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