Learn to debug software systematically using AI tools combined with test-driven development strategies. You will explore why AI debugging is useful for pattern recognition across large codebases, and understand the challenges with AI output including hallucination risks and the importance of verifying AI-generated suggestions against actual code behavior. The course covers project architecture analysis as a prerequisite for effective debugging, using documentation to provide AI tools with project-specific context that narrows suggestions and reduces hallucination. You will apply test-driven debugging where tests isolate buggy components, define bugs precisely through failing test cases, and verify fixes without regressions. The test-first approach demonstrates how writing a failing test before fixing a bug ensures the fix addresses the actual problem. The advanced module covers context gathering techniques that provide AI tools with logs, traces, and code history for accurate diagnosis, structured logging designed for both human and AI consumption, and finding debugging direction through contextual analysis rather than undirected AI queries. You will explore proactive bug hunting using AI to discover unknown defects by analyzing source code for potential issues ranked by severity. The course concludes with a complete framework integrating testing, context gathering, logging, and AI analysis into a unified debugging workflow. By completing this course, you will be able to combine test-driven development with AI-assisted debugging to find, reproduce, and fix bugs systematically.