Hello, I have been playing a little bit with the API and was wondering how to do optimized fine-tuning.
Basically, I want to train the
gpt-3.5 Turbo to communicate via
JSON input / output and to follow some rules. Those rules theoritically fit in 2000 tokens which is already to much to specify everytime.
Regarding input / outputs they have some constraints (eg. when returning a number, it must be between 0 and 100).
Now I went directly into fine tuning and there arise some interogations:
- It seems like we need to specify the
system prompt(so I guess the “rules”) every time which is not compatible with its length. Is there a way to overcome this ?
- My secondary thought was to simply the rule to 100 / 200 tokens and train as much as i can with a lot of inputs and outputs so it knows all type. This forces me to use quite longer prompt than necessary.
- My last idea was to simply provide input and output without constraints and enforce those constraint in the code instead of with the IA, seems also doable.
I’m wondering if this is the best idea to go this way for this project since one user session will be something like 3000 tokens.