Model distillation refers to a process in machine learning where a smaller, more efficient model (the "student") is trained to replicate the behavior of a larger, more complex model (the "teacher"). In the context of preventing hallucination in generative AI models, model distillation can help by transferring the knowledge and decision-making capabilities of the larger model to the smaller one, which may be less prone to generating incorrect or nonsensical outputs. This process often involves using the outputs of the teacher model as training data for the student model, allowing the student to learn to produce more accurate and reliable responses while being more efficient in terms of computation and resource usage.