AI can accelerate software delivery, but they do not understand what you need or your systems in the way an experienced engineer does. They generate plausible output from incomplete context - and plausible is not good enough to accelerate in your job (or just to keep it!).

This course develops the technical judgment required to use AI safely across the real software development lifecycle.

First you’ll understand the fundamentals by examining the mechanics behind language models, including tokenization, embeddings, vector spaces, attention, inference, and confabulation. From there, you’ll learn how to actually plan software features and guide AI through structured implementation workflows using AI-generated code, agent skills, and more.

The focus is production work: architectural planning, incremental implementation, repository integration, debugging, testing, code review, security, and maintainability. You’ll learn to define constraints, expose relevant system context, verify assumptions, detect subtle failures, and keep engineers accountable for the final result.

What's the bottom line?

This course is not about making you just code along without understanding the principles so that when you are done with the course you don’t know what to do other than watch another tutorial... No!

This course will push you and challenge you to go from a Junior Software Devleoper to someone that excels at using AI so much that you're treated like a Senior Software Developer 💪.