Use ChatGPT in a domain you're a relative expert in and you run into a million scenarios where it offers a "solution" that will do something close to what was described, but not quite - and you might even not immediately notice the problem as a domain expert. Even worse it may produce side effects suggestive that it is working as desired, when it's not.
In the not-so-secret world of Stack Exchange coffee pasta, people would have other skilled humans pointing these issues out. In the world of LLMs, you risk introducing ever more code that looks perfectly correct, but isn't. What happens at scale?
The net change in efficiency of LLMs will be quite interesting to see. Because unlike past technologies where there was only user error, we're dealing here with going to a calculator that will not infrequently give you an answer that's wrong, but looks right. And what sort of 'equilibrium' people will settle into with this, is still an open question.
Use ChatGPT in a domain you're a relative expert in and you run into a million scenarios where it offers a "solution" that will do something close to what was described, but not quite - and you might even not immediately notice the problem as a domain expert. Even worse it may produce side effects suggestive that it is working as desired, when it's not.
In the not-so-secret world of Stack Exchange coffee pasta, people would have other skilled humans pointing these issues out. In the world of LLMs, you risk introducing ever more code that looks perfectly correct, but isn't. What happens at scale?
The net change in efficiency of LLMs will be quite interesting to see. Because unlike past technologies where there was only user error, we're dealing here with going to a calculator that will not infrequently give you an answer that's wrong, but looks right. And what sort of 'equilibrium' people will settle into with this, is still an open question.