APPLIED GUIDE
How to evaluate AI output quality
Quality evaluation needs examples of the real task and a rubric that separates factual accuracy, completeness, instruction following, safety, and usefulness. Test before launch and after meaningful changes to the model, prompt, data, or workflow.
Recommended process
Quality evaluation needs examples of the real task and a rubric that separates factual accuracy, completeness, instruction following, safety, and usefulness. Test before launch and after meaningful changes to the model, prompt, data, or workflow.
- Collect representative successful and difficult cases.
- Write observable pass, fail, and escalation criteria.
- Use qualified reviewers and resolve disagreement.
Review checklist
Use this checklist before accepting the output or turning it into an action.
- Track severe errors separately from average scores.
- Repeat after model, prompt, data, or policy changes.
CONCRETE EXAMPLE
Observable result
A support workflow is tested on routine questions, ambiguous requests, outdated documents, privacy-sensitive cases, and adversarial wording before launch.
- Collect representative successful and difficult cases.
- Write observable pass, fail, and escalation criteria.
- Use qualified reviewers and resolve disagreement.
PRIMARY SOURCES
Check the basis for this guide.
NIST · 2023
AI Risk Management Framework 1.0
NIST · 2024
Generative AI Profile — NIST AI 600-1
NIST · 2023
NIST AI RMF Playbook
Frequently asked questions
Is one average score enough?
No. A strong average can hide rare but severe failures. Report critical failure rates and category-level results.
Is a citation enough to trust an answer?
No. Confirm that the cited source exists, is current, and actually supports the claim made.
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