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I'm dialing back my LLM usage

zed.dev

251–252 of 252 posts

Re: I'm dialing back my LLM usage

#251

One point I haven't seen made elsewhere yet is that LLMs can occasionally make you less productive. If they hallucinate a promising-seeming answer and send you down a path that you wouldn't have gone down otherwise, they can really waste your time. I think on net, they are helpful, especially if you check their sources (which might not always back up what they are saying!). But it's good to keep in mind that sometime…

That was my main reason for having dropped LLM coding assistance completely for now. It wasted so much of my time and energy. I got the occasional helpful response, but not before four that were almost correct, which of course is a much bigger time sink than when it's obviously wrong. Another fifteen minutes spent investigating a solution that turns out to be bogus.

I got the usual gaslighting that I'm just using the wrong models, or using them wrong, etc. But when I watched others, I found that their results were just as iffy, they just usually didn't bother to check them once they looked plausible.

Re: I'm dialing back my LLM usage

#252

Earlier quoted context omitted.

All it can do is predict the next token. So, yes.

If it's still 2020, then yes. In 2025 post-training like RLHF made it that these models do not just predict the next token, the reward function is a lot more involved than that.

Instruct models like ChatGPT are still token predictors. Instruction following is an emergent behavior from fine-tuning and reward modeling layered on top of the same core mechanism: autoregressive next-token prediction.
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