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

What is the difference between testing your model on a static CSV file (Offline) vs. monitoring real user "Thumbs Up/Down" feedback (Online)?

How do you calculate the ROI of a prompt change? If a new prompt is 5% more accurate but 50% more expensive in tokens, how do you decide if it’s worth it?

Why is a standard unit test (asserting that output == "expected") often a bad way to test an LLM? How do you handle a model that gives three different, but correct, answers to the same prompt?