Lessons from planning rater studies in AI: a five-step recipe
davidstutz92 · x · 2026-10-10
David Stutz shares lessons from a year of planning rater studies for generative AI systems. As tasks grow ambiguous and subjective, rater studies are now core to AI research, yet many engineers lack experience with raters. His five-step recipe: define the goal (one primary goal per study), pick the right data, design the questionnaire, recruit raters, and plan analysis upfront. He also stresses that AI health studies take far longer than expected — first-of-kind efforts, heavy coordination, and safety-first processes.
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