Don’t use LLMs in production for accuracy critical implementations without human oversight.

Don’t use LLMs in production for accuracy critical implementations without human oversight.

I almost want to repeat that a third time even.

They weirdly ended up being good at information recall in many cases, and as a result have been being used like that in cases where it really doesn’t matter much if they are wrong some of the time. But the infrastructure fundamentally cannot self-verify.

This is part of why I roll my eyes when I see employment of LLMs vs humans as an exclusionary binary. These are tools to extend and support human labor. Not replace humans in most cases.

So LLMs can be amazing at a wide array of tasks. Like I literally just saved myself a half hour of copying and pasting minor changes in a codebase by having Copilot automate generating methods using a parallel object as a template and the new object’s fields. But I also have unit tests to verify behavior and my own review of what was generated with over a decade of experience under my belt.

Someone who has never programmed using Copilot to spit out code for an idea is going to have a bad time. But they’d have a similar bad time if they outsourced a spec sheet to a code farm without having anyone to supervise deliverables.

Oh, and technically, my example doesn’t actually require you to know the correct answer before asking. It only requires you to recognize the correct answer when you see it. And the difference between those two usecases is massive.