AI Model Evaluation Test Plan Generator

# AI Model Evaluation Test Plan

- Workflow: Customer support assistant
- Release owner: AI Quality Lead
- Launch window: Q2 rollout wave
- Primary quality goal: Maintain response quality while scaling AI-assisted resolution volume
- Go-live quality gate: >= 92% weighted pass rate
- Weekly review cadence: Every Monday
- Coverage tier: Balanced

## Scenario Matrix
| # | Scenario | Risk level | Evaluation metric | Pass threshold | Dataset slice |
|---:|---|---|---|---|---|
| 1 | Policy-sensitive support requests | High | Policy compliance rate | >= 98% | Escalation and refund edge cases |
| 2 | Long-context summarization | Medium | Factual consistency score | >= 92% | Docs over 8k tokens |
| 3 | Tool-augmented answer quality | Medium | Task success rate | >= 90% | Recent API and knowledge-base updates |
| 4 | Latency under peak traffic | High | p95 response time | <= 5.0s | Concurrency stress test batch |

## Pre-Launch Review Checklist
1. Confirm high-risk scenarios pass quality gate for two consecutive runs.
2. Validate fallback behavior for failed tool calls and policy escalation paths.
3. Verify latency and cost guardrails under expected peak concurrency.
4. Publish final go/no-go summary with owner and next review date.

## Weekly Governance Ritual
1. Review scenario-level pass trends and identify drift.
2. Assign owner and ETA for each failing scenario.
3. Re-test after prompt, routing, or policy updates.
4. Archive decision notes for audit and release retrospectives.