The moment I can train Copilot on my codebase and improve its predictions, I’d gladly pay more than $20. Even now, I find that I can’t live without it simply as a slightly more intelligent auto-complete.
Prompting allows you to direct the model into a different way than the training data. If it were not so, LLMs would never solve problems that were not explicitly in their training set.
This is an issue with your prompting not the models.
If you tell someone "Do this thing" they'll just do it, LLM's too. And how it is done will probably be terrible.
If you ask someone "come up with 5 ways to accomplish this, compare and contrast between them, list pros and cons, think through best practices, maintainability, readability, security, performance and cost. Come to a final decision and then do it" their response will be different. So too will the LLM's. You can even have the LLM present its opinions and recommendation but pause and wait for your choice on the go-ahead.
Even if you do prompt it correctly, and it responded in the abstract with all these pros and cons, you cannot be reasonably sure that the code it also provided actually follows these best practices. It will just anytime mindlessly wander from "the best experts on the Internet are saying this" territory to "I just made this up" territory.
Compare that with googling a human-written stack overflow answer on the topic, there usually is some good soul who pointed out the inconsistency, if there is one.
Look, I like grinded meat. But the fact is, without a detailed analysis, it's hard to tell what's actually in it.
And?
As a Sr. Engineer, this is exactly how all the slop my Jr's and Mids send me looks. Copy pasted, Stack Overflow, and when I read through Jr code my mind is boggled. I want to shake them "did you even read your own code. do you even know what your code is doing? Why did you do this?" Forget style, best practices, etc,
In my opinion, LLM's produce code that is as good or better than most Jr engineers and as a Sr it is my responsibility to audit, review and test all code. As a Sr level engineer I spend 90% of my time judging/fixing/improving others code, and less than 10% of my time writing my own.
LLM is just another source, and unlike the Jr, I can quickly ask it why it did what it did or to refine it. You ask the Jr to iterate on the project and you won't hear from them until they mention a blocker at tomorrow's stand-up (or you just pair it out and spend 2 hours teaching them, while the LLM turns it around in 15 seconds. There is value in teaching of course, but we build quickly, too).
If it was a boolean, the limits you describe would certainly exclude some of the human programmers whose nonsense I have had to fix.
[0] and "generality", though "AGI when?" is a separate topic
The second issue I could work around but combined with the fact that it doesn't have context of my current codebase, my impression was that it would be a fantastic tool for someone going to school for CS but practically useless in a professional codebase.
If you understand how it works you can sometimes lay out your code in a way that makes it more likely to include the relevant examples to get the effect that you want.
There is no guidance or documentation for this at all!
I've been running a hack to help me see the prompts in order to better understand what's going on: https://gist.github.com/simonw/30fec0ac20244b4b921532b63b8b0...
In neovim w/ a medium-sized go project, I"m finding that it's really good at providing meaningful and at least directionally-accurate contextual suggestions. It seems that the plugin is providing a good bit of context, but maybe the vscode one doesn't?
I’m pretty sure it pulls context from at least the other open files in VS Code, if not the ones on disk. Hard to tell for sure.
It’s kind of like having an eager, well-read, but naive and over-caffeinated intern as a pair programmer.