Debugging with Generative AI

AI assistants can accelerate Python development, but only if you know how to direct them and check their work. This course shows you how to pair everyday AI tools with practical Python skills to move faster without sacrificing quality. Explore how to troubleshoot errors and confusing messages, speed up slow code through vectorization with libraries like NumPy and pandas, clarify unfamiliar code, and run security reviews to catch vulnerabilities such as path traversal. Along the way, learn how to spot when AI produces extraneous, inaccurate, or unsafe code, and how to sanity-check results so your programs stay correct and secure. By the end of this course, you’ll be equipped with a repeatable workflow for using AI as a capable assistant that you guide and validate.


Learning objectives
Use AI assistants to troubleshoot Python errors and interpret confusing messages.
Optimize slow code with vectorization and libraries such as NumPy and pandas.
Interpret and explain unfamiliar or inherited code with AI support.
Conduct security reviews to identify vulnerabilities like path traversal.
Evaluate AI-generated code for accuracy, efficiency, and unnecessary bloat.
Apply a verification workflow to keep AI-assisted code correct and secure.