You can always exercise decision making.
Realistically, people do a lot of Googling "parse Json in Python" and going with whatever pops out, like "import json" probably. But there's other options out there.
With a LLM, you're almost certainly also going to get the standard json module if you just ask for a JSON parser.
But nobody says you have to stop there. For instance if I say I want "the fastest JSON module", it goes for orgjson instead.
And if I wasn't happy with that, I could ask it to just write a benchmark using every module available and actually measure what's optimal for my hardware and my data file.
Choices don't go anywhere, and a smart user can take advantage to evaluate possibilities for whatever factors might matter -- speed, size, number of dependencies, etc. I'd say smartly used LLMs make for better choices because you can do research much more easily. Benchmarks, prototypes, asking it to search for you whether a given library has a good reputation or not and is well maintained, etc.
Realistically, people do a lot of Googling "parse Json in Python" and going with whatever pops out, like "import json" probably. But there's other options out there.
With a LLM, you're almost certainly also going to get the standard json module if you just ask for a JSON parser.
But nobody says you have to stop there. For instance if I say I want "the fastest JSON module", it goes for orgjson instead.
And if I wasn't happy with that, I could ask it to just write a benchmark using every module available and actually measure what's optimal for my hardware and my data file.
Choices don't go anywhere, and a smart user can take advantage to evaluate possibilities for whatever factors might matter -- speed, size, number of dependencies, etc. I'd say smartly used LLMs make for better choices because you can do research much more easily. Benchmarks, prototypes, asking it to search for you whether a given library has a good reputation or not and is well maintained, etc.