> [LLMs] save me a ton of time doing either boilerplate work
I hear this frequently from LLM aficionados. I have a couple of questions about it:
1) If there is so much boilerplate that it takes a significant amount of coding time, why haven't you invested in abstracting it away?
2) The time spent actually writing code is not typically the bottleneck in implementing a system. How much do you really save over the development lifecycle when you have to review the LLM output in any case?
I don't know about the boilerplate part but when you are e.g. adding a new abstraction that will help simplify an existing pattern across the code base something like Copilot saves a ton of time. Write down what has to happen and why, then let the machine walk across the code base and make updates, update tests and docs, fix whatever ancillary breaks happen, etc. The real payoff is making it cheaper to do exploratory refactors and simple features so you can focus on making the code and overall design better.
That's an interesting approach. You still have to review all the changes to make sure they're correct and that the code is maintainable, though. I could see this being a net savings on a legacy code base or a brand new system still in the "sketching" phase.
Yes, one of the reasons I like Copilot over some of the terminal-based systems I've seen is the changes are all staged for review in VS Code so you have all the navigation etc tools and can do whatever needs to be done before committing. It saves a lot of time, even on new features. I think of it like a chainsaw, powerful but a little bit imprecise.
I hear this frequently from LLM aficionados. I have a couple of questions about it:
1) If there is so much boilerplate that it takes a significant amount of coding time, why haven't you invested in abstracting it away?
2) The time spent actually writing code is not typically the bottleneck in implementing a system. How much do you really save over the development lifecycle when you have to review the LLM output in any case?