Section 050 · Chapter 7, Release Readiness for AI Systems
Human Review Workflows and Escalation Rules
The point of measurement is not just a score. It is knowing when automation is enough and when a human must step in.
What to do
- Define runnable checks that exercise LLM judge, human review, and escalation.
- Set acceptable outcomes and blocker failures for LLM judge, human review, and escalation before running the evaluation.
- Run representative cases for LLM judge, human review, and escalation and preserve the failures that would change the decision.
Evidence to preserve
- Preserve the inputs, versions, configurations, raw outcomes, and results for LLM judge, human review, escalation, human review workflows escalation rules needed to reproduce work on Human Review Workflows and Escalation Rules.
- Report results for LLM judge, human review, escalation, human review workflows escalation rules by relevant slice, separate blocker failures from averages, state uncertainty and blind spots, and connect the result to a release decision.
Expert note
In a real release review, track reviewer queue time, overturn rate, escalation precision, escalation recall, reviewer agreement, and category-level escalation load. A review workflow is itself a system that needs quality metrics. For particularly sensitive, complex, expensive, legally risky, safety-critical, or reputation-sensitive cases, require overlapping human review. One reviewer may not be enough; use second-reader workflows, expert tie-breakers, or independent review from multiple humans when the cost of a bad decision is high.
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Cite this page
Jason Arbon. "Human Review Workflows and Escalation Rules." Testing AI Knowledge Edition, section 50.
https://jarbon.ai/testing-ai/knowledge/ch050-human-review-workflows-escalation-rules.html