I've been thinking about this recently and it seems like the most enthusiastic boosters always suggest difference in results is a skill issue, but I feel like there are 4 factors which multiply out to influence how much value someone gets: - The quality of model output for _your particular domain / tech stack_. Models will always do better with languages and libraries they see a lot of than esoteric or proprietary - The degree to which "works" = "good" in your scenario. For a one off script, "works" is all that matters, for a long lived core library, there are other considerations. - The degree to which "works" can be easily (best yet, automatically) verified. - Techniques, existing code cleanliness, documentation etc.

Boosters tend to lay all different experiences at the feet of this last, yet I'd argue the others are equally significant.

On the other hand, if you want to get the best results you can given the first 3 (which are generally out of one's control) then don't presume there's nothing you can do to improve the 4th.