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hmm, is the powershell example something you don't expect to have to do again, so it's not worth really understanding the details? (and did you feel you needed
by eichin 3y ago
hmm, is the powershell example something you don't expect to have to do again, so it's not worth really understanding the details? (and did you feel you needed to verify the output, or was just running it enough? Not trying to judge here! Just curious because nngroup just published a user study that showed only a tiny percentage of people actually cross-checking LLM-assistant output - not that it was wrong, they weren't checking either, just that most of the users in their study didn't feel it was necessary.)
- jwestbury 3y agoIt's something which probably needs to be done again -- I included a few parameters in the script that allow it to be used for a limited set of similar workflows in the future. I read over the script, didn't spend loads of time understanding it, but the logic looked roughly correct. I generally read the code the LLM generates, then test it. I also try to include dry run modes whenever possible, so I can validate the mutating behaviour is correct before actually running any mutating commands -- I'm far more likely to do this with LLM-generated scripts than my own code, I've found, though perhaps that's just laziness. :)
- FranksTV 3y agoThere are a ton of development tasks that I do infrequently enough that I have to google how to do them every time, because I forget how to do them. For those things, I can just ask Chat-GPT to write the first draft of it, and it saves me about 80% of the time. I always have to end up doing a few edits, but it works out. Also dropping in an indecipherable page of logs and immediately getting the source of an error with at least a suggestion of a direction is really useful.