How do you design escalation systems for uncertain LLM outputs?

Escalation systems route low-confidence LLM outputs to stronger models or human reviewers.

When LLM confidence is low or risk is high, systems escalate the request. This can mean rerunning with a stronger model, triggering retrieval augmentation, or sending the query to a human-in-the-loop system. Escalation thresholds are dynamically tuned based on domain sensitivity and user tier.