The LLM tells me that they prefere the "older way" because it's more broadly compatible, that's ok if you are aiming for that. But If the programmer doesn't know about that they will be stuck with the LLM calling the shots for them.
The LLM tells me that they prefere the "older way" because it's more broadly compatible, that's ok if you are aiming for that. But If the programmer doesn't know about that they will be stuck with the LLM calling the shots for them.
We're not yet at a point where LLM coders will learn all your idiosyncrasies automatically, but those feedback loops are well within our technical ability. LLM's are roughly a knowledgeable but naïve junior dev; you must train them!
Hint: add that requirement to your system/app prompt and be done with it.
How many professional programmers don't have assemblers/compilers/interpreters "calling the shots" on arbitrary implementation details outside the problem domain?
https://vivekhaldar.com/articles/when-compilers-were-the--ai...
> Please write this thing
> Here it is
> That's asinine why would you write it that way, please do this
> I rewrote it and kept backward compatibility with the old approach!
:facepalm:
It's mostly useful when you work a lot with "legacy code" and can't just remove things willy nilly. Maybe that sort of coding is over-represented in the datasets, as it tends to be pretty common in (typically conservative) larger companies.
The less cruft and red herrings in the context, the better. And likewise with including key info, technical preferences, and guidelines. The model can’t read our minds, although sometimes we wish so :)
There are lots of simple tricks to make it easier for the model to provide a higher quality result.
Using these things effectively is definitely an complex skill set.