How would you handle hallucinations in a generative AI model deployed to users?

Focus on identifying the causes of hallucinations in generative AI models, such as data bias or model limitations. Discuss strategies for mitigating these issues, including improving training data quality, implementing user feedback loops, and refining model parameters. Emphasize the importance of user experience and trust, detailing how you would communicate limitations to users and provide mechanisms for reporting inaccuracies. Consider the balance between model performance and user safety, and outline how you would prioritize updates or fixes based on user impact.