> context length is too short to be really helpful for working on existing codebase.
This is kind of true, my approach is I spend a fairly large amount of time copy-pasting code from relevant modules back and forth into ChatGPT so it has enough context to make the correct changes. Most changes I need to make don't need more than 2-3 modules though.
> the output code is pretty much always broken in some way, and you need to be accustomed to doing code reviews to use them effectively.
I think this really depends on what you're building. Making a CRM is a very well trodden path so I think that helps? But even when it came to asking ChatGPT to design and implement a flexible data model it did a very good job. Most of the code it's written has worked well. I'd say maybe 60-70% of the code it writes I don't have to touch at all.
The slow typing is definitely a hindrance! Sometimes when it's a big change I lose focus and alt-tab away, like I used to do when building large C++ codebases or waiting for big test suites to run. So that aspect saps productivity. Conversely though I don't want to use a faster model that might give me inferior results.
> approach is I spend a fairly large amount of time copy-pasting code from relevant modules back and forth into ChatGPT
It can work, but what a terrible developer experience.
> I'd say maybe 60-70% of the code it writes I don't have to touch at all
I used to to write web apps so the ratio was even higher I'd say (maybe 80/90% of the code didn't need any modification) but the app itself wouldn't work at all if I didn't make those 10% changes. And you really need to read 100% of the code because you won't know upfront where those 10% will be.
> The slow typing is definitely a hindrance! Sometimes when it's a big change I lose focus and alt-tab away, like I used to do when building large C++ codebases or waiting for big test suites to run.
Yeah exactly, it's xkcd 303 but with “IA processing the response” instead of “compiling”. Having instant response was a game changer for me in terms of focus hence productivity.
> I don't want to use a faster model that might give me inferior results
As I said earlier, I didn't really feel the difference in quality so the switch was without drawbacks.
This is kind of true, my approach is I spend a fairly large amount of time copy-pasting code from relevant modules back and forth into ChatGPT so it has enough context to make the correct changes. Most changes I need to make don't need more than 2-3 modules though.
> the output code is pretty much always broken in some way, and you need to be accustomed to doing code reviews to use them effectively.
I think this really depends on what you're building. Making a CRM is a very well trodden path so I think that helps? But even when it came to asking ChatGPT to design and implement a flexible data model it did a very good job. Most of the code it's written has worked well. I'd say maybe 60-70% of the code it writes I don't have to touch at all.
The slow typing is definitely a hindrance! Sometimes when it's a big change I lose focus and alt-tab away, like I used to do when building large C++ codebases or waiting for big test suites to run. So that aspect saps productivity. Conversely though I don't want to use a faster model that might give me inferior results.