Debugging
This is a member-only chapter. Log in with your Signal Over Noise membership email to continue.
Log in to readModule 6 · Section 1 of 6
AI fails regularly. Models hallucinate, misread instructions, produce confident nonsense, and go off in entirely the wrong direction. Most people respond the same way: retry with a slightly different prompt, hope for a better result, repeat until something sticks.
That approach works occasionally. It does not help you understand why something went wrong, and it does not help you prevent the same problem next time.
Debugging is the discipline of tracing a failure to its root cause. It is systematic rather than intuitive — a method for moving from “this isn’t working” to “this specific thing is wrong for this specific reason, and here’s how to fix it.” Professional developers have practised this skill for decades. It is one of the most transferable thinking skills in computing, and it applies directly to how you work with AI.
This module draws on four stories: the Hubble Space Telescope’s flawed mirror, Toyota’s Five Whys technique, the parallels between stage magic and error handling, and Grace Hopper’s legendary moth. Each one illuminates a different facet of the debugging mindset. By the end, you will have a framework for diagnosing AI failures instead of just reacting to them — and a practical exercise to sharpen the skill.