How do you measure if the LLM actually answered the user’s question, even if the facts it provided were technically true?

If you are using an LLM to grade another LLM, why is it critical to provide a "multi-point rubric" rather than just asking "Is this answer good?"

Explain the concept of using a "Stronger" model (like GPT-4o or Claude 3.5 Sonnet) to grade a "Weaker" model’s output. What are the risks of "Self-Preference Bias" in this setup?

How would you automate the process of trying to make your model "break" or "hallucinate"?