Comment by AdieuToLogic
1 day ago
> I can think "oh it would be kinda nice to add this little tidbit of functionality in this code". Previously I'd have to spend loads of time googling around the question in various ways, wording it differently etc, reading examples or digging a lot for info.
Research is how people learn. Or to learn requires research. Either way one wants to phrase it, the result is the same.
> Now, I can have Claude help me write some code, then ask it about various things I can add or modify it with or maybe try it differently.
LLM's are statistical text (token) generators and highly sensitive to the the prompt given. More importantly in this context is the effort once expended by a person doing research is at best an exercise in prompt refinement (if the person understands the problem context) or at worst an outsourcing of understanding (if they do not).
> I'm fairly precise in what I ask it to do and that's only after I get it to explain how it would go about tackling a problem.
Again, LLM algorithms strictly output statistically generated text derived from the prompt given.
LLM's do not "explain", as that implies understanding.
They do not "understand how it would go about tackling a problem", as that is a form of anthropomorphization.
Caveat emptor.
We can go on all day about how an LLM doesn't explain and doesn't actually think. In the end though, I've found myself being able to do things better and faster especially given a codebase I have no experience in with developers who aren't able to help me in the moment given our timezone differences
> We can go on all day about how an LLM doesn't explain and doesn't actually think.
This is an important concept IMHO. Maintaining a clear understanding of what a tool is useful for and what it is not allows for appropriate use.
> In the end though, I've found myself being able to do things better and faster especially given a codebase I have no experience in ...
Here, I have to reference what I wrote before:
If you don't mind me asking two philosophic questions;
How can one be confident altering a codebase one has no experience with will become "better" without understanding it?
Knowing an LLM produces the most statistically relevant response to any given query, which is orthogonal to the concepts of true/false/right/wrong/etc., and also knowing one has no experience with a codebase, how can one be confident whatever the LLM responds with is relevant/correct/useful?
The thing about code is you can run it to confirm it does what you want it to do and doesn't do what you don't want it to do. Sprinkle in some software experience in there as well.
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