<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>AI治理 on C.CUI's Log</title><link>https://cuicaihao.github.io/zh/tags/ai%E6%B2%BB%E7%90%86/</link><description>Recent content in AI治理 on C.CUI's Log</description><generator>Hugo</generator><language>zh-Hans</language><lastBuildDate>Thu, 27 Aug 2026 00:00:00 +1000</lastBuildDate><atom:link href="https://cuicaihao.github.io/zh/tags/ai%E6%B2%BB%E7%90%86/index.xml" rel="self" type="application/rss+xml"/><item><title>企业 RAG 值不值得做：从场景选择到持续治理</title><link>https://cuicaihao.github.io/zh/posts/2026-08-23-is-enterprise-rag-worth-implementing-from-scenario-selection-to-ongoing-governance/</link><pubDate>Sun, 23 Aug 2026 07:00:00 +1000</pubDate><guid>https://cuicaihao.github.io/zh/posts/2026-08-23-is-enterprise-rag-worth-implementing-from-scenario-selection-to-ongoing-governance/</guid><description>企业 RAG 的难点不只是检索技术。企业要先判断业务场景是否值得使用 RAG，再确保知识有效、权限受控、证据能够支持答案、责任边界清楚，并持续衡量长期成本。本文从差旅制度案例出发，比较 RAG、传统搜索、人工支持与成熟软件产品，并讨论长上下文和智能体系统带来的新边界。</description></item></channel></rss>