alt Hacker NewsOne of the most annoying quirks of LLMs for me is the insistence on, after being corrected, loudly noting what wasn't done. After getting it to change something stupid in code, it will leave a comment in the code boasting about how it doesn't do the stupid thing, which to future readers reads like an 'asbestos free!' label on a cereal box.
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I have a contrarian take on this: if most people working on the codebase are using the same model, then these comments serve a very useful purpose: they tell the model's future self that despite its tendency towards this solution, it is incorrect and here's why. It encodes useful context right when the model needs it next to avoid making the same error.
I imagine this style of commenting is partially responsible for model improvement in SWE work, a trained trait through feedback, since it makes the comments actually useful. Not to you or me, but to an LLM.
Some of these errors are obvious to a human, that's why we find the comments unnecessary. But if everyone's using LLMs and said model tends to make this error, then they are very useful. It's like a comment you would leave for another human to help them avoid a footgun. It's just a LLM footgun, not a human one.
One of the reasons this is there is the "psychology" of the LLM performs better with these kinds of affirmations. If you've made a mistake once you're likely to make it again, so leave a note.
This indeed pollutes the code with a bunch of nonsense. One of the things I have it maintain instead is a "findings" document with lessons learned and such.
I’ve left so many PR reviews in the last few months that are essentially “this looks good but delete 80% of the comments.”