<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>RAG on Weiming's Blog</title><link>https://axerzone.cn/tags/rag/</link><description>Recent content in RAG on Weiming's Blog</description><generator>Hugo -- gohugo.io</generator><language>zh-cn</language><copyright>© 2026 Weiming</copyright><lastBuildDate>Wed, 15 Oct 2025 00:00:00 +0000</lastBuildDate><atom:link href="https://axerzone.cn/tags/rag/index.xml" rel="self" type="application/rss+xml"/><item><title>RAG 检索增强生成实践</title><link>https://axerzone.cn/posts/rag-practice/</link><pubDate>Wed, 15 Oct 2025 00:00:00 +0000</pubDate><guid>https://axerzone.cn/posts/rag-practice/</guid><description>&lt;h1 class="relative group"&gt;RAG 检索增强生成，让大模型不再胡说八道
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&lt;h2 class="relative group"&gt;RAG 是什么
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&lt;p&gt;RAG，Retrieval-Augmented Generation，检索增强生成。名字挺唬人，但思路很简单：先从你自己的知识库里检索相关内容，把检索结果塞进 Prompt 里一起给大模型，让模型基于这些内容来回答。&lt;/p&gt;</description></item></channel></rss>