Not sure how often models should be retrained feels like guesswork

Retraining often feels like guesswork because many teams do not have a clear signal for when the model has actually started to decay. Some retrain too early and waste effort, while others wait too long and let quality quietly slip.

The right cadence depends less on the calendar and more on behavior. Changes in user input, data distribution, business rules, and error patterns usually tell you more than a fixed monthly schedule ever will.

A practical approach is to monitor performance drift, production feedback, and edge-case failures. When those indicators move together, retraining becomes a response to evidence rather than a guess.