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

To mitigate hallucinations in deployed generative AI, I would implement a multi-pronged strategy. Retrieval Augmented Generation (RAG) is a key technique, enabling the model to ground its responses in factual, up-to-date information from a knowledge base and provide citations. Beyond RAG, I would focus on continuous monitoring of model outputs for factual inaccuracies, establish feedback loops with users to report and correct errors, and fine-tune the model with high-quality, verified data. Employing guardrails and content filters can also help prevent the generation of nonsensical or factually incorrect information.