How to Measure AI Assistant Accuracy When Customer Questions Change Over Time

An evaluation set can become stale as products, policies, and customer language evolve. A model may score well on old examples but fail on new intents or changed terminology.

What to check

Review recent query categories, unanswered questions, escalation reasons, and source changes. Compare evaluation coverage with actual usage while removing sensitive data and preserving representative edge cases.

Recommended next step

Refresh the evaluation set regularly with reviewed examples, track results by task and release, and include a human review step for newly emerging questions. Avoid treating a single score as proof of quality.

How Informatix Systems can help

Informatix Systems provides AI production operations and model evaluation services. We can review the environment, trace the problem through its dependencies, and help plan a practical resolution. Contact us with the system involved, when the issue began, and non-sensitive error details.