A Practical Guide for Developers Building Real AI Applications

Every developer can call a model API now. Almost none of them can ship an AI feature that actually holds up once real users hit it.

That gap - between a five-minute demo and a system that's reliable, fast, and doesn't quietly hallucinate in front of a customer - is where most AI projects die. Not because the model wasn't good enough. Because nobody taught the engineering discipline that sits between "I called the API" and "this works in production."

This book is that discipline, laid out in the order you actually need it. How to write prompts that behave like a real specification instead of a guess. How to force a model into structured, typed output your code can trust. How to build retrieval-augmented generation that grounds answers in your real data instead of the model's best guess - and why the RAG systems that fail almost always fail at retrieval, not generation. When fine-tuning is genuinely worth the cost, and the much more common case where it isn't. How to build agents that actually finish tasks instead of looping forever, and just as important, when not to build one at all. How to evaluate whether any of this is working, instead of trusting that it "looks fine." And how to keep the whole thing fast, affordable, and secure once real traffic hits it.

Every technique here is real: shipped provider features, published research, documented production systems at companies like GitHub, Notion, Perplexity, and Morgan Stanley - not speculation about what might work. No filler chapters, no recycled blog posts, no hype about AGI. Just the exact stack, in the exact order, that turns a working prototype into software you can actually put your name on.

If you're building with foundation models and tired of hitting the same wall past the demo stage, this is the book that gets you past it.