Book Review: AI Engineering

Review

Title: AI Engineering: Building Applications with Foundation Models

Author(s): Chip Huyen

This book provides a broad and structured overview of how modern AI systems built on top of large language models actually work.

Rather than focusing on specific tools or frameworks, it explains the core concepts behind foundation models and their applications. The book walks through topics such as:

• how training datasets are created and curated
• the transformer architecture behind modern LLMs
• finetuning approaches such as LoRA
• evaluation methods for generative models
• prompt engineering techniques
• retrieval-augmented generation (RAG)
• agentic patterns and tool use
• multimodal systems

It also touches on less frequently discussed areas like dataset engineering or inference pipelines and optimization techniques.

The explanations are technical but approachable. The book rarely dives deeply into formulas or source code, instead relying heavily on diagrams and visual explanations. This makes it easier to build a mental model of how these systems fit together.

One of the strongest aspects of the book is its production engineering perspective. It focuses on the lifecycle of building applications with foundation models: from data preparation and training, to evaluation, deployment, safety, and optimizations. Examples reference real tools and framework: models, RAG systems, agent frameworks, SDKs, gateways, and safety tooling. But the emphasis stays at all times on principles, rather than any particular stack.

While the book is mostly conceptual, it provided enough grounding for me to understand the source code of Andrej Karpathy's microgpt (small, but dense). I might try digging into nanochat next.

If you're trying to understand how LLM-based systems are designed end-to-end, this is a strong starting point. It won't teach you to implement a model from scratch, but it builds the mental framework needed to understand how the pieces fit together.