I’ve been writing code professionally for nearly two decades, and I love having copilot available in my IDE. When there is some boilerplate or a SQL query I just don’t want to write, it’ll oftentimes get me started with something reasonable that is wrong in a couple of subtle ways. I then fix it, laugh at how wrong it was, or use part of the proposed answer in my project.
If you’re a non-corder, sure it is pure danger, but if you know what you’re doing it can give you a little boost. Only time will tell if it makes me rusty on some basics, but it is another tool in the toolbox now.
For me personally, there is only two applications of LLMs in programming:
doing tasks I kinda know how to do, but don’t want to properly learn (recent example: generate pgf plots from csv data in matplotlib. 90% boilerplate, I last had to do it 3 years ago and vaguely remember some pitfalls so can steer the LLM in that direction. Will probably never again have to do this, so not worth the extra couple hours to properly learn
things I would ordinarily write a script for, but aren’t worth automating because they won’t come up in the future again (example: convert this Lua table to a Nix set)
Essentially, one-off things that you know how to check for correctness.
I’ve been writing code professionally for nearly two decades, and I love having copilot available in my IDE. When there is some boilerplate or a SQL query I just don’t want to write, it’ll oftentimes get me started with something reasonable that is wrong in a couple of subtle ways. I then fix it, laugh at how wrong it was, or use part of the proposed answer in my project.
If you’re a non-corder, sure it is pure danger, but if you know what you’re doing it can give you a little boost. Only time will tell if it makes me rusty on some basics, but it is another tool in the toolbox now.
For me personally, there is only two applications of LLMs in programming:
Essentially, one-off things that you know how to check for correctness.