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0 pointsYhippa1y ago0 comments

What you described was the exact same experience I had. I got so far off track in one of my conversations with corrections that I started all over again. It is neat that this technology can do it, but I probably would have been better off doing it manually to save time.

The other thing I’ve noticed is something you alluded to: the LLM being “confidently incorrect”. It speaks so authoritatively about things and when I call it out it agrees and corrects.

The more I use these things (I try to ask against multiple LLMs) the more I am wary of the output. And it seems that companies over the past user rushed to jam chatbots into any orifice of their application where they could. I’m curious to see if the incorrectness of them will start to have a real impact.

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SwiftyBug1y ago

One thing I noticed about this behavior of LLMs "seeing" their error when you correct them is that sometimes I'm not even correcting them, just asking follow up questions that they interpret as me pointing out some mistake. Example:

Me: - Write a Go function that will iterate over the characters of a string and print them individually.

~Claude spits out code that works as intended.~

Me: - Do you think we should iterate over runes instead?

Claude: – You are absolutely right! Sorry for my oversight, here's the fixed version of the code:

I just wanted to reason about possibilities, but it always takes my question as if I'm pointing out mistakes. This makes me feel not very confident in their answers.

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