Debugging AI Systems Is Not Like Debugging Code When I first started testing AI systems, I expected debugging to feel familiar. It didn’t. What We Expect from Debugging In traditional software, debugging is straightforward. Something breaks. You see: an error a crash a log You trace it, fix it, move on. There’s a clear signal. What Actually Happens in AI Systems AI systems don’t fail like that. They don’t crash. They don’t throw obvious errors. Instead, they: behave slightly differently follow unintended instructions produce outputs that seem “almost right” And everything still looks fine. The Subtlety Problem This is what makes debugging AI difficult. Failures are subtle. You don’t always notice them immediately. For example: A system might: partially ignore instructions respond in an unexpected tone change behavior under certain inputs Nothing breaks. But something is off. Why This Matters If failures were obvious, they would be easier to fix. But silent failures are dangerous.…