I know, I know, "skill issue"/"you're holding it wrong". And maybe that's vacuously true, in that it's so hard to guess what will produce correct output, because LLMs are not an abstraction layer in the way that we're used to. Prior abstraction layers related input to output via a transparent homomorphism: the output produced for an input was knowable and relatively straightforward (even with exotic optimization flags). LLMs are not like that. Your input disappears into a maze of twisty little matmuls, all alike (a different maze per run, for the same input!) and you can't relate what comes out the other end in terms of the input except in terms of "vibes". So to get a particular output, you just have to guess how to prompt it, and it is not very helpful if you guess wrong except in providing a wrong (often very subtly so) response!
Back in the day, I had a very primitive, rinky-dink computer—a VIC-20. The VIC-20 came with one of the best "intro to programming" guides a kid could ask for. Regarding error messages it said something like this: "If your VIC-20 tells you something like ?SYNTAX ERROR, don't worry. You haven't broken it. Your VIC-20 is trying to help you correct your mistakes." 8-bit 6502 at 1 MHz. 5 KiB of RAM. And still more helpful than a frontier model when it comes to getting your shit right.
