This path is for builders who need more than prompt tips. It emphasizes model limits, tool use, workflow structure, and the discipline needed to evaluate AI-assisted systems in production.
The builder habit is to treat AI behavior as something that needs evidence. A useful feature should have a clear boundary, representative test examples, expected behavior, regression cases, and release gates before it touches a production workflow.
The practical workflow material also teaches research provenance. Builders practice keeping source links, confidence labels, and verification ownership visible before research becomes an architecture note, backlog item, implementation assumption, or release recommendation.
Use the foundations material to understand the model and data boundaries, the practical workflow material to decide where AI belongs, and the agents-and-architecture course to design boundaries, failure handling, evaluation loops, and rollout decisions.