When Code 'Works' but Doesn't Always: The Hidden Cost of Untested Assumptions

This story exposes a critical gap in modern development: the difference between code that works and code that always works. AI-assisted coding can embed unexamined assumptions about data (like non-empty lists), and the absence of proper error handling creates invisible failures that are expensive in production. It's a powerful reminder that thorough edge-case testing and defensive programming are non-negotiable, especially when leveraging AI-generated code.