Yeah, their statement just isn't true. With enough instruction, I've been able to get great output from models. I think that's the key: with detailed, pointed instructions, the output will match.

how do you know it matches? You did read it then?

Indeed, I'm not using LLM output without thorough review.

After reading a bunch of other comments, it sounds like people are referring to letting agents go wild and code whatever off a limited prompt. I'm not using LLMs like that; I'm generally interacting only via conversations with pretty detailed initial prompts. My interactions with the chat after that are corrections/guiding prompts to keep it on point and edit the prompt output from time to time.

Which defeats the purpose of using a LLM in the first place. Same as writing by hand but with a bill for tokens.

This is a fun new position to move the goalposts too, I suppose, but it doesn’t make much sense to me. If I can use AI to plan, implement, test, document, refine, release, and maintain a feature, with review, in 20% of the time it would take me without AI, how exactly does it “defeat the purpose” of using an LLM? My purpose for using the LLM is to solve problems faster, and it does that. What’s yours?

Exactly this. So far it's helped identify blind spots in my thinking, as well as educate me further on the techniques and frameworks I'm already using. It's been tremendously helpful at developing very well thought out _and tested_ software.