Many approaches to context-aware computing have focused on providing means for developers to define application contexts. In these approaches, correct application behavior depends on the developer defining the right contexts. The application cannot adapt incorrectly defined contexts as they are statically defined by the developer. We propose a method by which an application can use feedback to evaluate its contexts and adapt them where necessary. This results in more accurate context definitions and should lead to improved application performance. We discuss how this is achieved using Q-learning, and present a scenario and experimental results to support our approach.