在AI/大模型落地评估中，准确率（Accuracy）衡量的是"答得对不对"，场景覆盖率（Coverage / Scenario Coverage）衡量的是"管不管得到"。二者从不同维度评价AI系统的有效性。

一、准确率（Accuracy）——质量指标

指AI在它决定回答的样本中，输出结果与标准答案一致（业务可接受）的比例。

Accuracy = 回答正确数 / AI自主回答总数

• 注意：工业落地中通常排除转人工/兜底（Fallback）的样本，只算AI自己处理的那些。

• 类别不平衡时需配合 Precision、Recall、F1 综合看，单纯 Accuracy 可能有误导性。

• 生成式任务常用事实一致性/无幻觉率替代传统 Accuracy。

二、场景覆盖率（Scenario/Coverage Rate）——范围指标

通常指AI能自主承接处理的业务量占总业务量（或适合AI处理的业务量）的比例。

两种常见口径：

此外还有测试视角的场景覆盖度​ = 已覆盖测试场景数 / 应测业务场景数（含正常/异常/边界），用于衡量测试充分性。

三、核心区别一览

四、准确率与覆盖率的权衡（Trade-off）

AI系统常通过设置置信度阈值来决策"自己答"还是"转人工"：

• 调高阈值​ → 只回答高置信度问题 → 准确率↑，覆盖率↓（保守策略）

• 调低阈值​ → 尝试回答更多问题 → 覆盖率↑，准确率↓（激进策略）

• 最佳实践：先对简单/高频场景做到高准确率→逐步扩大覆盖；对低置信度样本走人工兜底或RAG增强。

落地评估建议

1. 按细分场景统计准确率，不要只看全局均值——不同子场景（如差旅vs采购报销）差异可能很大。

2. 明确定义覆盖率分母，建议用"AI适合处理的业务量"作基准，排除明显需人工介入的长尾case。

3. 关注兜底率/转人工率作为补充——覆盖率 + 兜底率 ≈ 100%（在适域内）。

4. 分类模型配合 Precision/Recall/F1；生成式模型补充幻觉率、BLEU/ROUGE 等。

如果你有具体场景（比如智能客服、OCR审核、大模型问答等），可以告诉我，我帮你细化对应的指标计算公式和验收阈值参考。

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