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Okay, how am I supposed to use them "correctly"? Because me explaining step by step, more so than a junior developer, how to do a small task in an existing codebase for it to get it wrong not once, not twice, not three times, but more is not a productivity boost.

And here's the difference between someone like me and an LLM: I can learn and retain information. If you don't understand this, you don't have a correct understanding of LLMs.

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It is entirely true that current LLMs do not learn from their mistakes, and that is a difference between eg an LLM and a human intern.

It is us, the users of the LLMs, that need to learn from those mistakes.

If you prompt an LLM and it makes a mistake, you have to learn not to prompt it in the same way in the future.

It takes a lot of time and experimentation to find the prompting patterns that work.

My current favorite tactic is to dump sizable amounts of example code into the models every time I use them. I find this works extremely well. I will take code that I wrote previously that accomplishes a similar task, drop that in and describe what I want it to build next.

You seem to be assuming that the thing I'm learning is not "Stop using LLMs for this kind of work".

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