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378 points todsacerdoti | 1 comments | | HN request time: 0.218s | source
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xnorswap ◴[] No.44984684[source]
I won't say too much, but I recently had an experience where it was clear that when talking with a colleague, I was getting back chat GPT output. I felt sick, like this just isn't how it should be. I'd rather have been ignored.

It didn't help that the LLM was confidently incorrect.

The smallest things can throw off an LLM, such as a difference in naming between configuration and implementation.

In the human world, you can with legacy stuff get in a situation where "everyone knows" that the foo setting is actually the setting for Frob, but with an LLM it'll happily try to configure Frob or worse, try to implement Foo from scratch.

I'd always rather deal with bad human code than bad LLM code, because you can get into the mind of the person who wrote the bad human code. You can try to understand their misunderstanding. You can reason their faulty reasoning.

With bad LLM code, you're dealing with a soul-crushing machine that cannot (yet) and will not (yet) learn from its mistakes, because it does not believe it makes mistakes ( no matter how apologetic it gets ).

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ivanjermakov ◴[] No.44984959[source]
> I was getting back chat GPT output

I would ask them for an apple pie recipe and report to HR

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1. chasd00 ◴[] No.44988112[source]
> I would ask them for an apple pie recipe and report to HR

i do this sometimes except i reply asking them to rephrase their comment in the form of a poem. Then screenshot the response and add it as an attachment before the actual human deletes the comment.