Ah, interesting. I myself have made my own library to create callable “prompt functions” that prompt the model and validate the JSON outputs, which ensures type-safety and easy integration with normal code.
Lately, I’ve shifted more towards transforming ChatGPT’s outputs. By orchestrating multiple prompts and adding human influence, I can obtain responses that ChatGPT alone likely wouldn’t have come up with. Though, this has to be balanced with giving it the freedom to pursue a different thought process.
If you don’t mind me asking, does your tool programmatically do the “whittling down” process by talking to ChatGPT behind the scenes, or does the user still talk to it directly? The former seems like a powerful technique, though tricky to pull off in practice, so I’m curious if anyone has managed it.
I’m happy to have contributed an entire 0.001% of the posts on this platform
Reddit now:
What’s your all-time favorite video game?
u/totallynormaluser: “I’m sorry, but as an AI language model, I don’t have personal preferences or emotions, so I don’t have the ability to have a favorite video game.”