Choose evaluation cases before tuning
Build a representative set of questions or tasks, including ambiguous inputs and cases where information is missing. Agree what a useful response should contain and which errors matter most. Keep some cases aside from iterative development so the team can examine whether an improvement generalizes beyond the examples used to make it.
Evaluate the workflow, not only the answer
Review whether the output is supported, understandable and routed to the right user. For a document assistant, an answer without a reliable source reference may be unsuitable even when its wording appears plausible. Record how a reviewer checks the result and what happens when the information is incomplete. Those operating steps are part of system quality.
Keep reviewing after release
Information, users and working conditions change. Assign ownership for feedback, monitoring and re-evaluation when the workflow changes materially. NIST’s AI risk guidance emphasizes evaluation before deployment and continued assessment during operation. A practical review plan should define what is checked and who acts on a problem rather than relying on a vague promise to monitor performance.
Bring it to the next conversation.
- Include ambiguity and missing information.
- Check evidence and operating steps.
- Assign responsibility for ongoing review.
Further reading: NIST AI Risk Management Framework.
From the Hexifyer team. Examples are illustrative and do not identify client projects. Project requirements and professional responsibilities are agreed for each engagement.