企业 RAG 之后：知识系统进入持续评估阶段After Enterprise RAG: knowledge systems enter continuous evaluation
检索准确率只是起点。真正值得维护的企业知识系统，需要版本、权限、证据链与知识时效管理。Retrieval accuracy is only the starting point. A knowledge system worth maintaining needs version control, permissions, evidence chains and freshness management.
检索准确率只是起点。真正值得维护的企业知识系统，需要版本、权限、证据链与知识时效管理。
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01 / 为什么检索准确率不够了
RAG 上线后，团队发现「能检索」不等于「能长期维护」。检索准确率是单点指标，真正的系统要回答：知识是否过期、权限是否隔离、证据是否可追溯。
02 / 四个关键能力层
- 版本：知识库需要版本控制，回滚到上一版。
- 权限：敏感知识按角色隔离，不混入公开内容。
- 证据链：每条答案可追溯来源文档。
- 时效：自动标记过期条目，定期刷新。
03 / 下一步：持续评估
把评估从「离线 benchmark」升级到「真实任务持续评估」：成功率、成本、失败模式都要进入雷达。
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参考：Y 站技术全景沙盘「Data & Knowledge」栏目。
Retrieval accuracy is only the starting point. A knowledge system worth maintaining needs version control, permissions, evidence chains and freshness management.
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01 / Why retrieval accuracy is no longer enough
Once RAG ships, teams learn that being able to retrieve is not the same as being able to maintain. Accuracy is a single-point metric. A real system has to answer harder questions: is this knowledge stale, is sensitive content isolated by role, can we trace an answer back to its source.
02 / Four capability layers that matter
- Version control — the knowledge base is versioned, and you can roll back to a previous state
- Permissions — sensitive knowledge is isolated by role, never mixed into public content
- Evidence chain — every answer traces back to a source document
- Freshness — stale entries are flagged automatically and refreshed on a schedule
03 / Next: continuous evaluation
Move evaluation off the offline benchmark and onto real tasks. Success rate, cost and failure modes all belong on the radar.
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Reference: the Data & Knowledge section of the UWON technology landscape.