Insights
Why Offline Scores Miss Production AI Failures
Why offline metrics can miss production AI failures, and how replay rows and failure taxonomies make evaluation more actionable.
Offline scores can hide where the failure starts
A single aggregate score may move in the right direction while a high-risk subset gets worse.
The score also may not show whether the defect came from retrieval, context assembly, tool use, policy, prompt behavior, or final answer generation.
Production failures often involve context
User phrasing, fresh content, long-tail tasks, handoff paths, permissions, and escalation expectations are easy to underrepresent offline.
A useful eval pulls representative traces or sanitized examples back into replay rows so the team can see the failure again.
The fix is not to abandon offline evaluation
Offline evaluation still matters. The problem is pretending one score explains release readiness.
Use offline scores, replay rows, failure taxonomies, and release gates together so the evidence can guide engineering work.