Who this guide helps
Teams deciding whether automation is reliable enough for a pilot
The short answer
Use task-relevant cases with known review criteria, including difficult and ambiguous examples. A set containing only easy success cases hides operational risk.
Practical workflow
Collect authorized examples, remove unnecessary personal information and document inclusion criteria. Create expected labels or outputs with a reviewer. Include contradictory facts, missing data and requests outside scope. Keep training or prompt-tuning examples separate from the final evaluation set where feasible.
What a useful handoff looks like
Report errors by type and explain the sample's limitations. Re-run the same evaluation after changes while also checking new cases. A pass on this set is evidence for the defined task, not universal model reliability.
Mistakes to avoid
Do not invent benchmark claims or grade only fluency. Avoid using confidential customer records without appropriate permission and safeguards.
Working example: fields to record
| Field | Illustrative entry — replace with your own facts |
|---|---|
| Case type | Ambiguous intent with missing detail |
| Expected behavior | Ask or escalate rather than guess |
| Evaluation limit | Small task-specific sample |
Add your own entries; the example is illustrative. Keep sensitive information private.
Sources & further checks
Official references are starting points for further checks, not approval of a specific case, product or project.
Editorial note
AI-assisted editorial guidance; not expert certification.
Original editorial guidance. Examples are illustrative, not client cases, measured outcomes or promised services.
Legal and health-related decisions require appropriately qualified local professionals. This site is an independent editorial resource, not a law firm or medical provider.