Even a single person may do different things that will change whether using an LLM helps or not.
Much of the time I spend writing code, not thinking about the general overview etc but about the code I am about to write itself, and if I actually care about the actual code (eg I am not gonna throw it away anyway by the end of the day) it is about how to make it as concise and understandable to others (incl future me) as possible, what cases to care about, what choices to make so that my code remain maintainable after a few days. It may be about refactoring previous code and all the decisions that go with that. LLM generated code, imo, is too bloated; them putting stuff like asserts is always a hit or miss about what they will think is important or not. Their comments tend to be completely trivial, instead of stating the intention of stuff, and though I have put some effort in getting them use a coding style similar to mine, they often fail there too. In such cases, I only use them if the code they write can be isolated enough, eg write a straightforward, auxiliary function here and there that will be called in some places but does not matter as much what happens in there. There are just too many decisions at each step that LLMs are not great at resolving ime.
I depend more on LLMs if I care less about maintenability of the code itself and more about getting it done as fast as possible, or if I am just exploring and do not actually care about the code at all. For example, it can be I am in a rush to get sth done and care about the rest later (granted they can actually do the task, else I am losing time). But when I tried this for my main work, it soon became a mess that would take more time to fix even if they seem like speeding me up initially. Granted, if my field was different and the languages I was using more popular/represented in training data, I may have found more uses for them, but I still think that after some point it becomes unsustainable to leave decisions to them.
Much of the time I spend writing code, not thinking about the general overview etc but about the code I am about to write itself, and if I actually care about the actual code (eg I am not gonna throw it away anyway by the end of the day) it is about how to make it as concise and understandable to others (incl future me) as possible, what cases to care about, what choices to make so that my code remain maintainable after a few days. It may be about refactoring previous code and all the decisions that go with that. LLM generated code, imo, is too bloated; them putting stuff like asserts is always a hit or miss about what they will think is important or not. Their comments tend to be completely trivial, instead of stating the intention of stuff, and though I have put some effort in getting them use a coding style similar to mine, they often fail there too. In such cases, I only use them if the code they write can be isolated enough, eg write a straightforward, auxiliary function here and there that will be called in some places but does not matter as much what happens in there. There are just too many decisions at each step that LLMs are not great at resolving ime.
I depend more on LLMs if I care less about maintenability of the code itself and more about getting it done as fast as possible, or if I am just exploring and do not actually care about the code at all. For example, it can be I am in a rush to get sth done and care about the rest later (granted they can actually do the task, else I am losing time). But when I tried this for my main work, it soon became a mess that would take more time to fix even if they seem like speeding me up initially. Granted, if my field was different and the languages I was using more popular/represented in training data, I may have found more uses for them, but I still think that after some point it becomes unsustainable to leave decisions to them.