| # What Should an AI Benchmark Measure? | |
| A single score is easy to compare, but often hides the trade-offs that matter | |
| in practice. Should a benchmark emphasize average quality, worst-case errors, | |
| latency, cost, or performance across different user groups? | |
| For generative systems, I find it useful to separate correctness, usefulness, | |
| and response time, then inspect examples where those goals conflict. What | |
| metrics or evaluation practices have helped you make better model choices? | |