Example Concept: To handle ambiguous input, implement a fallback mechanism that first asks clarifying questions to gather more context. Use machine learning models to identify intent with confidence scores, and when scores are low, trigger a response that seeks additional user input to refine understanding. Additionally, continuously update the training dataset with real user interactions to improve the model's ability to recognize and disambiguate intents.