We are helping our users with qa tasks involving code generation, where the answers may be either JSON, executable code, or markdown discussions involving the same. We are tuning for a bunch of tools following that pattern so our users don't have to.
It's easy to make a labeled training set for grading our homework (catching regressions, ...) in the case of classifiers, and that's basically what the blog post showed.
What about for the above qa tasks? We can ask GPT4 whether a generated A was a good answer for a Q, but that's asking it to grade itself. Likewise, in the code case, we can write unit tests for the answers. (Trick: we use the former to more quickly do the latter.) But I feel like there has to be better ways
Another: OpenAI always updates models based on use, so we have to be sure our tests are real holdout sets that never get back to them...