Task classification - given a set of classes, return the most likely class for each task

Guardrails - check the text against a set of guardrail checks (questions)

Search - Given a search criteria, I first have an LLM generate a query that would go to the decision model. This query is a rubric that scores each document. The highest scoring documents become the search hits. This is fast and cheap, so you can use it as a cross-encoder for your entire set, instead of just as a final re-ranker.