[zamadatix]

You may not need that last 10% on a hobby project. If you do and it's insurmountable with AI+you then you're no worse off than when it was insurmountable with just you.

Outside that context, the better way to use the tools is as a superpowered stack overflow search. Don't know how ${library} expects you to ${thing} in ${language}? Rather than just ask "I need to add a function in this codebase which..." and pastes it into your code ask "I need an example function which uses..." and use what it spits out as an example to integrate. Then you can ask "can I do it like..." and get some background on why you can/can't/should/shouldn't think about doing it that way. It's not 100% right or applicable, especially with every ${library}, ${thing}, and ${language} but it's certainly faster to a good answer most of the time than SO or searching. Worst case failure? You've spent a couple minutes to find you need to spend a lot of time reading through the docs to do you one off thing yourself still.

[imiric]

That's the way I currently use them. But, just like with SO, the code could be outdated and not work with the specific version of the library you're using, or just plain wrong. There's no way to tell it to show you code using version X.Y. The code could even be a mix of different versions and APIs, or the LLM might be trained on outdated versions, etc.

Even worse, the LLM will never tell you it doesn't know the answer, or that what you're trying to do is not possible, but will happily produce correct-looking code. It's not until you actually try it that you will notice an error, at which point you either go into a reprompt-retry loop, or just go read the source documentation. At least that one won't gaslight you with wrong examples (most of the time).

There are workarounds to this, and there are coding assistants that actually automate this step for you, and try to automatically run the code and debug it if something goes wrong, but that's an engineering solution to an AI problem, and something that doesn't work when using the model directly.

> Worst case failure? You've spent a couple minutes to find you need to spend a lot of time reading through the docs to do you one off thing yourself still.

It's not a couple of minutes, though. How do you know you've reached the limit of what the LLM can do, vs. not using the right prompt, or giving enough context? The answer always looks to be _almost_ there, so I'm always hopeful I can get it to produce the correct output. I've spent hours of my day in aggregate coaxing the LLM for the right answer. I want to rely on it precisely because I want to avoid looking at the documentation—which sometimes may not even exist or be good enough, otherwise it's back to trawling the web and SO. If I knew the LLM would waste my time, I could've done that from the beginning.

But I do appreciate that the output sometimes guides me in the right direction, or gives me ideas that I didn't have before. It's just that the thought of relying on this workflow to build fully-fledged apps seems completely counterproductive to me, but some folks seem to be doing this, so more power to them.