Planning maintenance windows

Your AI system won't always provide appropriate information at the right time. Issues with relevance often cause context errors, conflicts between the system working as intended and users' real needs. Low confidence situations occur when models can't fulfill tasks due to uncertainty, lack of data, or unstable information. A flight price predictor might fail for next year's prices because conditions keep changing.

High confidence but irrelevant output creates different problems. Booking a trip for a funeral and receiving "fun vacation activity" suggestions shows high confidence in the wrong context. These relevance errors frustrate users even when technically correct. Plan for times when your system can't provide good results. Explain why certain outputs couldn't be given and provide alternative paths. "Not enough data to predict prices for next year. Try checking again in a month," acknowledges limitations helpfully. Create feedback mechanisms for users to report relevance issues. When the system works technically but fails contextually, user feedback helps improve future performance.