This is a living textbook for the craft of AI engineering. Not a collection of tutorials copied from documentation, and not a course that wraps a YouTube playlist in a paywall. It is a place I built to teach the things I actually had to figure out the hard way: how to think about models as infrastructure, how to design systems that stay honest when they talk to users, and how to ship AI software that holds up in production.
The material here is organized into paths and standalone lessons. A path walks you through a connected set of ideas in order. A standalone lesson is self-contained, useful on its own, and linked to related paths when they exist. You can start anywhere, but if you are new to AI engineering I recommend reading the AI Engineering Foundations path first.
I update this place as my thinking develops. Some lessons are finished. Some are marked as drafts. I would rather show you a draft that is honest about what it does not cover than a polished lesson that glosses over the hard parts.
If you want to follow along as new material lands, the simplest way is to join the list. Otherwise, start with the first lesson below.
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