What is a "Golden Dataset" (or Ground Truth set), and how many samples should it ideally contain before you can trust your evaluation metrics?

How do you programmatically check if an LLM is making things up that aren't in the provided search results?

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

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?