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canonical: https://wavect.io/zh/services/ai-evaluation-llmops/
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description: "评估现有 LLM、RAG 和智能体系统。Wavect 围绕真实任务建立回归数据、调用追踪、发布检查和成本监控。"
image: "https://wavect.io/img/general/bak/open_graph_preview.jpg"
---

AI 评估与 LLMOps

# 在客户遇到问题之前，看清 AI 的变化。

一次提示词修改可能改善演示，却破坏无人重新测试的流程。我们帮助已有 AI 应用的团队，把预期行为转化为可重复评估，并将结果、调用轨迹和运行成本用于发布决策。

合作范围

## 定义任务、观察执行、检查变更。

1. **记录预期行为** 整理代表性任务、已知失败和评审标准。
2. **评估每次变化** 把模型、提示词、检索和工具版本放在一起测试。
3. **结合上下文运营** 建立调用追踪、成本预算、告警和事件反馈。

直接说明

AI 评估与 LLMOps 通过版本化任务集、回归检查、调用追踪及延迟和成本信息，评估并管理已有 LLM 应用。范围覆盖模型、提示词、检索和工具变化带来的应用行为变化。

适合以下需求

- 缺少可靠回归证据，却需要持续发布 AI 产品的团队
- 难以解释智能体失败原因或成本上升的负责人

其他需求路径

- 尚需完整开发的新 AI 产品
- 针对越权访问和滥用路径的独立安全评估

## 衡量客户真正需要完成的任务

我们明确哪些步骤使回答有用：找到正确来源、提供依据、选择正确工具，以及在缺乏权限或证据时停止。数据集包括常见任务、已经观察到的失败和困难边界。预期标准区分允许的表达差异与错误的业务操作，而不是只检查回答看起来是否流畅。

## 选择能够支撑发布决定的检查

确定性断言适合结构和工具参数；人工评审适合含义模糊的质量判断。基于模型的评分器可以辅助，但需要与已审核样本校准。记录数据集版本和测试条件，让结果可以比较。权限和滥用路径的独立检查属于 [AI 安全](/zh/services/ai-security/) 。

## 沿着系统链路定位失败请求

我们连接模型调用、检索结果、工具执行、重试和回退路径，形成团队可以查看的轨迹。采集生产内容之前先约定脱敏和保留方式。在可行情况下按完成有效任务计算延迟和成本，并计入失败尝试。追踪设计应能解释问题，而不默认收集全部敏感输入。

## 示例验收：更便宜的模型省略了必要步骤

在一个示例比较中，候选模型每次调用成本较低，却在回答前跳过了规定的信息查询。回归集暴露该遗漏，阻止它进入这一生产流程。评审同时考虑任务成功、延迟和完整运行成本。调用轨迹帮助确定问题出在检索、路由还是回答生成。

## 让下一次发布更容易判断

交付包括评估数据集、评分规则、运行配置、仪表盘和发布标准。我们建立流程，将审核过的生产失败纳入评估，同时避免污染留出的测试数据。负责人决定哪些质量变化可接受、何时回退。新产品交付由 [AI 智能体与产品开发](/zh/services/artificial-intelligence/) 承接；训练模型生命周期基础设施属于 [MLOps](/zh/services/mlops/) 。

## 开始前的常见问题

### 可以评估不是你们开发的应用吗？

可以。我们需要可调用的测试路径、预期行为，以及足够的可观测信息。初始评估先确定哪些内容可测，再决定增加哪些追踪能力。

### 评估能证明模型绝不会产生幻觉吗？

不能。评估反映特定任务和条件下的表现。我们记录覆盖限制，并为证据不足的情况定义拒答、人工复核或回退方式。

### 必须保存所有生产会话吗？

不必。我们定义最低必要追踪字段、脱敏、访问和保留规则。一些评估可使用合成样本或经过批准的脱敏实例。

### 可以降低模型成本吗？

可以针对同样的任务标准比较路由、缓存、提示词和模型。无法正确完成流程的便宜回答，不应被视为已经实现的节省。

## 选择合适的下一步

- [检查权限与 AI 滥用路径](/zh/services/ai-security/)
- [构建新的 AI 产品](/zh/services/artificial-intelligence/)
- [管理训练模型的生命周期](/zh/services/mlops/)

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