You'll see this in the workplace too. It's very hard to explain to the not-most-productive people that the most productive people on the team are also usually making the most mistakes. They'll always be happy to just sit back and criticize the mistakes. It's the only way they can be better.

Sorry, my comment wasn't LLMs in general, it was this specific pair of LLMs at this specific time.

I've been trying the same prompt since ChatGPT was released, and I keep revisiting it.

They are getting better at writing code, Bard's addition of sourcing information is very important to me.

I do this so I can see if it will help _me_ in what I'm trying to do, and where the failure modes are. Their failure modes are very sneaky and very dangerous for the kind of programming I do.

I would use them to maybe give me a first pass at some code in a language I have zero familiarity with, and only to solve general problems.

I'm thinking throw-away, one-off code where the operational range is known up front and fully middle of the road. I'm thinking reports.

I might try to get it to write something useful in R, I have no knowledge of that.

I can see them as really handy refactoring tools. They take instruction pretty well. They just need to be stable in the code they produce - they keep reintroducing bugs you've instructed them to fix. Changing to an edit stream against code instead of re-generating the code might be a fix?