Michael discusses the complexities of addressing hallucination in large language models, emphasizing the need for foundational research to integrate interventions directly into the model training process. He highlights the limitations of current approaches, such as guardrail models, and predicts a future where constraints are embedded from the outset, enhancing the reliability of AI outputs.
