the snippets are examples. You can ask hundreds of variations of similar, but different, complex questions and the LLM can adjust the example for that need.
I don't have a snippet for, "find all 500's for the meltano service for duckdb syntax errors", but it'd easily nail that given the existing examples.
but if I know enough about the service to write examples, most of the time I will know the command I want, which is less typing, faster, costs less, and doesn't waste a ton of electricity.
In the other cases I see what the computer outputs, LEARN, and then the functionality of finding what I need just isn't useful next time. Next time I just type the command.
LLMs are really good at processing vague descriptions of problems and offering a solution that's reasonably close to the mark. They can be a great guide for unfamiliar tools.
For example, I have a pretty good grasp of regular expressions because I'm an old Perl programmer, but I find processing json using `jq` utterly baffling. LLMs are great at coming up with useful examples, and sometimes they'll even get it perfect the first time. I've learned more about properly using `jq` with the help of LLMs than I ever did on my own. Same goes for `ffmpeg`.
LLMs are not a substitute for learning. When used properly, they're an enhancement to learning.
Likewise, never mind the idiot CEOs of failing companies looking forward to laying off half their workforce and replacing them with AI. When properly used, AI is a tool to help people become more productive, not replace human understanding.
I don't have a snippet for, "find all 500's for the meltano service for duckdb syntax errors", but it'd easily nail that given the existing examples.