AnthusAI

You can't fine-tune Jev itself but you can train an ML model that uses outputs from Jev as inputs. Which does enable you to 'fine-tune' your overall model.

You can also improve your Jev classifications based on your ongoing data if you're labeling it continuously, especially if you're explaining the reasoning in the feedback labels. You can identify new elements of the rubric and add them to the list of classifications that Jev produces, and then those become new features for your ML model.

Two levers of control for using data to make a Jev-based classifier model continuously better-aligned.

Fine tuning is generally not the best way to teach an LLM new knowledge. RAG is still more appropriate. Fine tuning is generally more effective for controlling the format of the responses but it's not going to teach the model a lot of new concepts. The model can learn how to handle new vocabulary through fine tuning but it's not a great way to teach the model new facts. Giving it access to a knowledge base is a better way to do that.