Datasets, labeling & ground truth
The idea: Ground truth is built by people: clear guidelines, human labelers, agreement checks, and curated 'gold' sets — increasingly seeded by models but verified by humans. Quality beats quantity.
What you'll be able to do: You can explain how labeled datasets and ground truth are built, and why label quality is critical.
The problem it solves: Your eval is only as good as its answers. Where does 'the right answer' even come from?
Builds on: Evals: proving it works
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