Firstly, by measuring data drift and analyzing user behavior, UpTrain identifies which prompts/questions were unseen by the model or the cases where the user was unsatisfied with the model output. It automatically collects those cases for the model to retrain upon.

Secondly, you can use the package to define a custom rule and filter out relevant data sets to retrain ChatGPT for your use case.

Say you want to use LLM to write product descriptions for Nike shoes and have a database of Nike customer chats:
a) Rachel - I don't like these shoes. I want to return them. How do I do that?
b) Ross - These shoes are great! I love them. I wear them every day while practicing unagi.
c) Chandler - Are there any better shoes than Nike? 👟 😍
You probably want to filter out cases with positive sentiments or cases with lots of emojis. With UpTrain, you can easily define such rules as a python function and collect those cases.

I am working on an example highlighting how all the above can be done. It should be done in a week. Stay tuned!