What you'll learn:
- Spot exactly where AI helps in debugging and where it wastes time
- Use AI tools like ChatGPT, Claude Sonnet, and Copilot to speed up root cause analysis
- Feed AI clean, targeted context so it gives useful answers instead of guesses
- Write unit tests, edge cases, fuzz tests, and benchmarks with AI’s help
- Catch AI’s mistakes before they cause bigger problems in production
- Audit AI-generated fixes using diff tools, linters, and static analysis
- Identify and prevent AI-driven security risks like injection flaws or weak validation
- Run full-loop debugging with AI from bug report to verified patch
- Build and refine your own AI-powered debugging checklist for real-world projects
- Blend AI assistance with your own expertise to become a faster, more reliable debugger
Debugging is hard. Adding AI into the mix can make it faster-or much riskier. Tools like Copilot, and other AI assistants can suggest fixes, but they can also introduce new bugs or hide existing ones. This course shows you how to use AI as a debugging partner without losing control of your code.
What this course gives you
A practical system for combining classic debugging skills with AI assistance. You’ll learn when AI can speed you up, when it can mislead you, and how to always keep verification in your workflow.
What you’ll learn
How AI fits into modern debugging
Ways to feed AI clean context for better suggestions
Techniques to isolate problems with and without AI
Using AI to generate and expand test coverage
Spotting AI “fixes” that hide new bugs
Fact-checking AI explanations with tools and checklists
Recognizing and managing AI security risks
Building a full-loop debugging workflow with AI agents
Creating your personal debugging checklist
How we’ll work
You’ll:
Run activities that train you to isolate code issues with AI help
Practice generating unit tests and fuzz tests with AI
Audit AI patches and spot hidden mistakes
Learn to manage AI as if it were a junior developer
Build and refine your own debugging playbook
Why it matters
By the end, you’ll be able to treat AI as a multiplier, not a replacement. You’ll debug faster, with stronger safeguards, and with a clear system for verifying every AI-assisted change.
No hype. No blind trust. Just real debugging skills, enhanced with AI.
