It’s been a tremendous help to me as I relearn how to code on some personal projects. I have written 5 little apps that are very useful to me for my hobbies.
It’s also been helpful at work with some random database type stuff.
But it definitely gets stuff wrong. A lot of stuff.
The funny thing is, if you point out its mistakes, it often does better on subsequent attempts. It’s more like an iterative process of refinement than one prompt gives you the final answer.
“Oh, I see the problem. In order to correct (what went wrong with the last implementation), we can (complete code re-implementation which also doesn’t work)”
I used to have this issue more often as well. I’ve had good results recently by **not ** pointing out mistakes in replies, but by going back to the message before GPT’s response and saying “do not include y.”
It’s incredibly useful for learning. ChatGPT was what taught me to unlearn, essentially, writing C in every language, and how to write idiomatic Python and JavaScript.
It is very good for boilerplate code or fleshing out a big module without you having to do the typing. My experience was just like yours; once you’re past a certain (not real high) level of complexity you’re looking at multiple rounds of improvement or else just doing it yourself.
Personally I find all LLMs in general not that great at writing larger blocks of code. It’s fine for smaller stuff, but the more you expect out of it the more it’ll get wrong.
I find they work best with existing stuff that you provide. Like “make this block of code more efficient” or “rewrite this function to do X”.
It’s been a tremendous help to me as I relearn how to code on some personal projects. I have written 5 little apps that are very useful to me for my hobbies.
It’s also been helpful at work with some random database type stuff.
But it definitely gets stuff wrong. A lot of stuff.
The funny thing is, if you point out its mistakes, it often does better on subsequent attempts. It’s more like an iterative process of refinement than one prompt gives you the final answer.
Or it get stuck in an endless loop of two different but wrong solutions.
Me: This is my system, version x. I want to achieve this.
ChatGpt: Here’s the solution.
Me: But this only works with Version y of given system, not x
ChatGpt: <Apology> Try this.
Me: This is using a method that never existed in the framework.
ChatGpt: <Apology> <Gives first solution again>
I used to have this issue more often as well. I’ve had good results recently by **not ** pointing out mistakes in replies, but by going back to the message before GPT’s response and saying “do not include y.”
It’s incredibly useful for learning. ChatGPT was what taught me to unlearn, essentially, writing C in every language, and how to write idiomatic Python and JavaScript.
It is very good for boilerplate code or fleshing out a big module without you having to do the typing. My experience was just like yours; once you’re past a certain (not real high) level of complexity you’re looking at multiple rounds of improvement or else just doing it yourself.
Personally I find all LLMs in general not that great at writing larger blocks of code. It’s fine for smaller stuff, but the more you expect out of it the more it’ll get wrong.
I find they work best with existing stuff that you provide. Like “make this block of code more efficient” or “rewrite this function to do X”.