What you'll learn

Let a Python code base grow long enough and it ends up carrying the same logging, timing, permission, retry, and caching logic from one function to the next. AI assistants write the decorators that get rid of it, fast, and they're mostly right. What you still need is a dependable way to tell when that's true, and to notice when working code quietly stops working as the project evolves. In this course, Python Decorators and AI-driven Meta-programming, you’ll gain the ability to move cross-cutting concerns into correct, typed, async-safe decorators, and to prompt, verify, and refine the code an AI pair programmer hands you. First, you’ll explore building single- and multi-argument decorators with functools.wraps to handle logging, timing, and access control. Next, you’ll discover how prompting an AI pair programmer produces nested decorator closures you can generate, debug, and test, how mutation checks prove its tests can actually fail, and how class-based decorators and parameterized factories get shaped to pass mypy --strict. Finally, you’ll learn how to make those decorators work with asyncio and hold per-task state across concurrent tasks through contextvars. When you’re finished with this course, you’ll have the skills and knowledge of Python decorators and AI-assisted meta-programming needed to reshape real service code into readable, performant, reliably typed modules built on decorators.