Generative AI handles ambiguous prompts by relying on techniques such as prompt clarification, contextual inference, and probabilistic sampling to generate meaningful responses. Here are the key steps you can follow:
- Clarification: Requests additional user input to resolve ambiguity.
 
- Contextual Inference: Uses surrounding context or training data to interpret the most likely intent.
 
- Temperature and Beam Search: Explores diverse outputs by adjusting sampling strategies.
 
Here is the code snippet you can refer to:
In the above code, we are using the following key points:
- Clarification Requests: Generates a follow-up prompt to resolve ambiguity.
 
- Diverse Outputs: Produces multiple interpretations using temperature sampling.
 
- Context-Aware Responses: Leverages learned patterns to infer the most likely intent.
 
Hence, this approach ensures meaningful and user-aligned content generation even when faced with ambiguous prompts.