and fwiw, i'm also not alone in this observation. I can at least remember 2 times in the last month that, other colleagues have cited this exact same benefit.

e.g - a complicated algo that someone wrote 3 years ago, that's working well enough but has always had subtle bugs. over a 2 day workshop, we start first by writing a bunch of (meaningful) tests with an LLM. then ask the LLM about portions of the code and piecing together why a certain bit of logic existed or was written a certain way, add more tests to confirm working behavior, then start refactoring and changing the algo (also with an LLM).

much of this is similar to how we'd do it without LLMs. but no one has bothered to improve/change it cause the time investment & ROI didn't make sense (let alone the cognitive burden in gathering context from git logs or old timers who have nuggets of context that could be pieced together). with LLMs a lot of that friction can be reduced.