29Every new model reports 90%+ on the same benchmarks. Why don't those scores help us choose, and what should we use instead?▼mediumNewCursorOpenAI◆ premiumSaturated and contaminated benchmarks separate models less than the noise in your own traffic. Use public scores to shortlist and a private, refreshed eval to decide.Practise this question →Read answer →
39You're the PM for a public benchmark your lab will release. How do you keep it useful for more than a year?▼expertNewGoogle DeepMindOpenAISarvam AI◆ premiumBenchmarks die from leakage, label errors and saturation. Hold back a private split, audit labels with experts, score abstention, watch the public-private gap and publish when you will retire it.Practise this question →Read answer →