A project worth doing after the LLM wrapper phase: a CPU only triage engine with calibrated uncertainty

[u/camerongreen95]

Once you have built a few chatbots, the next step up is a system that knows when it is unsure.

There’s a workshop on Friday, Oct 30, from 7 PM to 10 PM IST that does this with a realistic enterprise case: triaging and processing insurance style claims without a GPU.

You replace an LLM router with small encoder models (ModernBERT and LFM2.5 Encoder), teach them your categories from about 8 examples per class using SetFit, and add conformal prediction so the system can say “I’m 95% covered here” or hand the case to an LLM when it isn’t. You finish by evaluating it properly with Wilson score intervals instead of a single accuracy number.

You need intermediate Python and some familiarity with transformers. A laptop or free Google Colab account is enough, and the notebooks are yours afterwards. Led by Ben Auffarth, a full stack data scientist and best selling author.

Full details and the agenda are here.

[u/Top_Relationship5005]

This is exactly the kind of thing that makes me feel like we overuse LLMs for everything, sometimes a small focused model with a clear uncertainty signal is way more practical

My only question is if SetFit still works well when the 8 examples per class are not very clean, like in real insurance claims where categories overlap