Building Agent-Powered Applications

Building Agent-Powered Applications is the kind of book you wish existed when you first started trying to make sense of the LLM ecosystem. It opens with a grounded overview of ML and NLP fundamentals - not exhaustive, but just enough to build shared vocabulary before diving into the real substance.

The middle chapters are where the book earns its keep. The coverage of prompt engineering goes well beyond "write clearer prompts" - it walks through chain-of-thought, tree-of-thought, few-shot strategies, and multi-prompt chaining with enough context to understand the trade-offs. The RAG chapter is similarly practical: it distinguishes between lexical, semantic, and hybrid retrieval, explains TF-IDF and vector databases without oversimplifying, and keeps the focus on engineering decisions rather than just theory. Fine-tuning gets honest treatment too - the book is clear about when it's the right tool versus when RAG or prompting will serve you better, and it covers the real costs of full versus partial fine-tuning.

The final stretch, covering agent architecture and evaluation, is the strongest part. The agent chapter pulls everything together - memory design, tool orchestration, MCP, multi-agent coordination - and frames it as a software engineering problem, not just an AI one. That framing matters. It treats reliability, error handling, and human-in-the-loop design as first-class concerns rather than footnotes.

The evaluation chapter deserves special mention because it's often the first thing cut from books like this. Here it gets proper attention: offline vs. online evaluation, lexical and semantic metrics, LLM-as-judge approaches, and safety/adversarial testing all feature. The core message - that evaluation should be defined at the start of a project, not bolted on at the end - is exactly right.

If you're building AI-powered applications and want a single resource that takes you from foundational concepts all the way through to production-ready agents, this is the one to read.