Principle

Symptom You fit a model. It scores 94% on training data and 71% on held-out data. So you simplify: fewer parameters, more regularization. Training drops to 88%, held-out rises to 84%. You simplify further and both drop. Somewhere in there was an optimum, and you found it by trial and error, with a validation set and patience. ...

Symptom You fix a typo in an error message. Four days later a customer escalates: their alerting pipeline greps for the old string and has gone quiet. Or you make a function faster, and a test suite that had been green for two years starts failing intermittently — not because the function is wrong, but because it used to be slow enough to hide a race. ...