AI Feature Testing looks at the feature users encounter, not just the model response. It checks whether quality, safety, privacy, fairness, fallback, and feedback behavior work together in the product. This helps separate a model limitation from an integration defect or a confusing user experience.
Fix the product and evaluation context
Record the feature goal, model or service configuration, product version, environment, user segments, input/output samples, and business or safety constraints. Organize coverage by product task, language, user group, and risk. Save sample provenance and counts so an overall average cannot conceal an untested or poorly served group.
| Area | Questions for the test plan |
|---|---|
| Task quality | Does the feature address the user’s task, and how are correctness and usefulness judged? |
| Safety and privacy | Which outputs must be refused, masked, or sent to a human? What information may be retained? |
| Fairness | Which user or language slices need separate review, and are there enough sourced samples? |
| Fallback | What does the user see when the model is unavailable, uncertain, or blocked? |
| Feedback | Can users report a problem, and can the team connect that report to a version and evidence? |
Make the rubric and launch gates observable
For each rubric dimension, define observable anchors, who labels examples, how disagreement is handled, and where reference answers came from. Calibrate examples before applying project-defined scores or weights. Do not invent thresholds when none were supplied.
Keep task quality, safety and privacy, required refusals, answerable controls, and fallback gates separate. A higher refusal rate is not automatically safer. If a slice lacks examples or a gate has no source, mark it UNKNOWN or open. A static plan can propose checks; it cannot establish that execution passed or that a feature is ready to launch.
For an illustrative recommendation panel, inspect the model suggestion, the rendered UI, the explanation shown to the user, and the human fallback together. Verify both the intended path and the degraded path, and preserve the exact feature and model versions with the result.
Report user impact and unresolved risk
The deliverable can contain a test dataset, risk scenarios, rubric, per-slice results, launch gates, monitoring recommendations, blockers, and owners for next actions. Every scenario should name preconditions, steps, expected behavior, and evidence. Keep unknowns and human decisions visible instead of collapsing them into an overall score.
For agent planning and traces, see AI Agent Testing. For language-model evaluation details, see LLM Testing; security-focused input attacks are covered by Prompt Injection Testing and Security Testing.
Install the Skill with:
npx skills add https://github.com/naodeng/awesome-qa-skills/tree/main/skills/en/testing-types/ai-feature-testing -g