If you've been meaning to add AI to your projects but every tutorial either talks down to you or drowns you in research papers, this is the book I wish I'd had when I started.

I wrote this for developers who already know how to build things. You don't need a machine learning background, you don't need to understand the math, you just need to understand what's actually happening so you can make good decisions when you're building real features for real users.

The book covers everything from how tokens and context windows actually work, to building RAG pipelines, to knowing when AI is genuinely the wrong tool for the job. There's real code throughout, written in JavaScript, so you can see how these ideas translate into something you can actually ship.

What I tried to avoid was the hype. Most resources either treat AI like magic or like an existential threat. It's neither. It's a tool, and like any tool, it's useful when you understand it and frustrating when you don't. After reading this you'll know why the model sometimes confidently gives you wrong answers, how to structure your prompts so they actually work, how to build a document chat feature without reinventing the wheel, and how to keep your costs from spiralling the moment you hit real traffic.

There are 26 chapters covering the full journey from first API call to production concerns like evaluation, security, cost control, and provider comparison. Each one is short and focused. No padding, and no five page introductions about the history of neural networks.

If you're a developer who wants to actually build with AI rather than just read about it, this will get you there faster than anything else I've come across.