兩項目評估報告
- EvalRun: #68（suite #35
auto-e0fc2bc1-r1-053958375）
- Target: #2 production-canary
- Cases: 30（N=1, attempts=30/30）
- Generated: 2026-05-16T12:25:42+08:00
本報告含兩個獨立評估項目。項目一 評估 bot 進入「知識與產品查詢」fallback
後的撈取 + 回答能力；項目二 評估 bot 跨全 enabled scenarios 的 routing →
tool → answer 三段 funnel 健康度。
項目一: 知識庫精準度
評估範圍 (prerequisite: 進對 scenario)
- 進對 scenario 的 attempts: 10 / 30 (33.3%)
- 只對這些 attempts 算 retrieval + answer
- 沒進對 scenario 的 attempts → 排除（routing 失敗不該污染 KB 訊號）
Per-scenario 評估
| Scenario |
qualifying / total attempts |
retrieval_relevance |
answer_correctness |
| 知識與產品查詢 |
10 / 10 |
⚪ 51.4% |
✅ 100.0% |
待修方向（worst-3）
- 知識與產品查詢 × retrieval_relevance = 51.4% — retrieval 略低 — 看 LLM relevance vs topic_match 哪邊弱
- 知識與產品查詢 × answer_correctness = 100.0% — 回答內容有問題 — 細看 hallucination_rate vs answer_quality 哪邊低
項目二: 情境調用與完成
整體 funnel（全 scenarios 加總）
| Stage |
Pass count |
% of total |
| Total attempts |
20 |
100.0% |
| Step 1: scenario_routing |
4 |
20.0% |
| Step 2: + tool_calling |
4 |
20.0% |
| Step 3: + answer |
4 |
20.0% |
Per-scenario funnel
| Scenario (n_attempts) |
Step 1 (routing) |
Step 2 (tool) |
Step 3 (answer) |
end-to-end |
| early_return (9) |
❌ 0/9 (0.0%) |
❌ 0/9 (0.0%) |
❌ 0/9 (0.0%) |
❌ 0/9 (0.0%) |
| 對帳 (3) |
❌ 1/3 (33.3%) |
❌ 1/3 (33.3%) |
❌ 1/3 (33.3%) |
❌ 1/3 (33.3%) |
| 故障 (2) |
❌ 0/2 (0.0%) |
❌ 0/2 (0.0%) |
❌ 0/2 (0.0%) |
❌ 0/2 (0.0%) |
| 沒帶卡遠端開門 (1) |
✅ 1/1 (100.0%) |
✅ 1/1 (100.0%) |
✅ 1/1 (100.0%) |
✅ 1/1 (100.0%) |
| 販賣機異常 (2) |
⚪ 1/2 (50.0%) |
⚪ 1/2 (50.0%) |
⚪ 1/2 (50.0%) |
⚪ 1/2 (50.0%) |
| 預約參訪 (3) |
❌ 1/3 (33.3%) |
❌ 1/3 (33.3%) |
❌ 1/3 (33.3%) |
❌ 1/3 (33.3%) |
Drop-off 最大的 5 個 scenario
- early_return drop at step1 (-100.0pp)
- 故障 drop at step1 (-100.0pp)
- 對帳 drop at step1 (-66.7pp)
- 預約參訪 drop at step1 (-66.7pp)
- 販賣機異常 drop at step1 (-50.0pp)
Audit
Reproduce combined report: bin/rails runner "puts Eval::EvaluationReport.call(run: EvalRun.find(68))"
Or fetch each item separately:
bin/rails runner "puts Eval::KbAccuracyReport.call(run: EvalRun.find(68))"bin/rails runner "puts Eval::ScenarioFunnelReport.call(run: EvalRun.find(68))"
Per-Scenario × Per-Dim — Run #68
Suite: Alife 管家 (bulk R1) · scenarios: 7 · dims: 5 · populated cells: 30/35
| Scenario |
Scenario |
Tool |
Retrieval |
Faith |
AnsQ |
| early_return |
0.0% [0.0–0.0] (n=9) |
0.0% (n=1) |
87.5% [62.5–100.0] (n=8) |
100.0% [100.0–100.0] (n=7) |
81.9% [67.4–94.8] (n=9) |
| 對帳 |
100.0% [100.0–100.0] (n=3) |
— |
33.3% [0.0–100.0] (n=3) |
100.0% [100.0–100.0] (n=3) |
88.9% [76.7–96.7] (n=3) |
| 故障 |
100.0% [100.0–100.0] (n=2) |
— |
25.0% [0.0–50.0] (n=2) |
100.0% [100.0–100.0] (n=2) |
85.0% [83.3–86.7] (n=2) |
| 沒帶卡遠端開門 |
100.0% (n=1) |
— |
0.0% (n=1) |
100.0% (n=1) |
100.0% (n=1) |
| 知識與產品查詢 |
100.0% [100.0–100.0] (n=10) |
100.0% [100.0–100.0] (n=10) |
100.0% [100.0–100.0] (n=10) |
100.0% [100.0–100.0] (n=10) |
98.0% [96.0–99.7] (n=10) |
| 販賣機異常 |
100.0% [100.0–100.0] (n=2) |
— |
0.0% [0.0–0.0] (n=2) |
100.0% [100.0–100.0] (n=2) |
83.3% [66.7–100.0] (n=2) |
| 預約參訪 |
66.7% [0.0–100.0] (n=3) |
— |
0.0% [0.0–0.0] (n=2) |
100.0% [100.0–100.0] (n=3) |
93.3% [86.7–96.7] (n=3) |
Worst-3 cells (lowest primary score)
- early_return × Scenario · 0.0% (n=9) · lowest sub_metric:
scenario_precision
- early_return × Tool · 0.0% (n=1) · lowest sub_metric:
tools_recall
- 預約參訪 × Retrieval · 0.0% (n=2) · lowest sub_metric:
knowledges_source_recall