Evaluator Model

A practical example

Example: every AI-drafted follow-up email is scored by a judge model for tone, factual consistency with the CRM record, and missing next steps; outputs below threshold are routed to a human reviewer.

What to evaluate before investing

• Ask whether the judge's criteria are configurable per use case, and who can edit them.
• Test judge reliability: do two runs with the same output produce consistent scores, and does the judge catch seeded errors you plant on purpose?
• Ask what happens to flagged outputs — are they blocked, queued for review, or only logged for later analysis?

Limitations and tradeoffs

Tradeoff: judge models have their own biases and can miss errors or penalize valid answers, and each judgment adds cost and latency.

Treat judge scores as a filter that reduces manual review, not as proof of quality.