<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom" xmlns:content="http://purl.org/rss/1.0/modules/content/"><channel><title>Chunking on SiBlog</title><link>https://sinimite.work/tags/chunking/</link><description>Recent content in Chunking on SiBlog</description><image><title>SiBlog</title><url>https://sinimite.work/images/og-default.svg?v=20260525-210321</url><link>https://sinimite.work/images/og-default.svg?v=20260525-210321</link></image><generator>Hugo -- 0.156.0</generator><language>zh-cn</language><lastBuildDate>Sat, 01 Aug 2026 00:00:00 +0900</lastBuildDate><atom:link href="https://sinimite.work/tags/chunking/rss.xml" rel="self" type="application/rss+xml"/><item><title>RAG 评测指标速查表</title><link>https://sinimite.work/posts/rag-evaluation-metrics-cheat-sheet/</link><pubDate>Sat, 01 Aug 2026 00:00:00 +0900</pubDate><guid>https://sinimite.work/posts/rag-evaluation-metrics-cheat-sheet/</guid><description>一份 RAG 评测指标速查表，覆盖 Recall、nDCG、Context Precision、Faithfulness、Citation、拒答、安全、延迟与成本等指标，帮助 AI Application Engineer 建立可落地的评测门禁。</description></item><item><title>RAG 系统中的 Chunking 最佳实践</title><link>https://sinimite.work/posts/rag-chunking-strategies-evaluation/</link><pubDate>Wed, 15 Jul 2026 18:05:16 +0900</pubDate><guid>https://sinimite.work/posts/rag-chunking-strategies-evaluation/</guid><description>一篇关于 RAG Chunking 策略、参数选择、上下文保留与评估流程的技术参考。</description></item><item><title>企业级 RAG 系统搭建指南</title><link>https://sinimite.work/posts/enterprise-rag-system-building-guide/</link><pubDate>Wed, 15 Jul 2026 16:35:23 +0900</pubDate><guid>https://sinimite.work/posts/enterprise-rag-system-building-guide/</guid><description>介绍企业级 RAG 系统的数据摄取、混合检索、权限控制、生成、评估与可观测架构。</description></item><item><title>RAG 系统全景指南：从 0 到生产级的完整设计流程</title><link>https://sinimite.work/posts/rag-system-complete-guide/</link><pubDate>Fri, 27 Mar 2026 15:06:20 +0900</pubDate><guid>https://sinimite.work/posts/rag-system-complete-guide/</guid><description>面向 AI 应用工程学习者的 RAG 全景指南，系统讲清数据摄入、分块、检索、重排序、生成、评估与生产运维，并给出从 0 到上线的实现路径。</description></item><item><title>RAG Chunking 最佳实践指导文档</title><link>https://sinimite.work/posts/rag-chunking-best-practices/</link><pubDate>Wed, 18 Mar 2026 20:40:55 +0900</pubDate><guid>https://sinimite.work/posts/rag-chunking-best-practices/</guid><description>一份面向 AI Application Engineer 的 RAG chunking 实战指南，覆盖默认参数、主流切分策略、评估流程与生产实践。</description></item><item><title>Anthropic Contextual Retrieval 阅读笔记</title><link>https://sinimite.work/posts/anthropic-contextual-retrieval-reading-notes/</link><pubDate>Wed, 11 Mar 2026 09:00:00 +0900</pubDate><guid>https://sinimite.work/posts/anthropic-contextual-retrieval-reading-notes/</guid><description>一篇围绕 Anthropic Contextual Retrieval 的阅读笔记，覆盖 chunk 语义丢失问题、实验 benchmark、ground truth 评估方法与工程化 RAG 最佳实践。</description></item></channel></rss>