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EVALUATION

Why AI evaluations need difficult examples

Create categories for routine, incomplete, conflicting, and unsupported inputs.

AI Revenue Cycle editorial teamArchive date: 1 min read

Newly prepared for this archive on September 22, 2026. The archive date is an editorial grouping, not an original publication date.

The everyday problem

A collection of clean examples can hide the behavior that matters most in daily work. Missing documents, contradictory fields, and unusual responses may determine whether a workflow is usable. An evaluation that excludes these cases tests a narrower problem than the team will eventually operate.

A practical approach

Create categories for routine, incomplete, conflicting, and unsupported inputs. Define the expected response for each category, including when the system should stop. Review errors by category so that a strong average does not obscure a failure in a critical exception path.

Try this with your team

Add one deliberately incomplete fictional example to your test set. Specify the clarification or escalation you expect before running the test.

Keep reading

Make AI outcomes understandable to the next teamScale an AI workflow without copying its assumptionsTurn repeated exceptions into process improvements