How do you design confidence scoring systems for LLM outputs?

Confidence scoring evaluates how reliable an LLM output is using model probabilities, consistency checks, and secondary evaluators.

Since LLMs do not explicitly output calibrated confidence, systems approximate it using token probabilities, ensemble disagreement, retrieval support strength, and verifier models. This score determines whether an output is shown, rewritten, or rejected. It is critical in high-stakes domains like legal or finance.