Eval dataset delay

Evaluation datasets delay progress when they are slow to build, hard to update, or too disconnected from actual usage. If the dataset takes too long to assemble, experimentation gets stuck waiting for a benchmark before it can learn anything useful.

This problem often means the team has not streamlined how examples are collected and labeled. Once the process becomes manual and fragmented, evaluation starts lagging behind product development.

A healthier setup keeps the dataset closer to production reality and easier to refresh over time. That makes benchmarking more practical and more trustworthy.