<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Embedding on 黄文卓 | DevOps Engineer</title><link>https://socake.github.io/tags/embedding/</link><description>Recent content in Embedding on 黄文卓 | DevOps Engineer</description><generator>Hugo -- gohugo.io</generator><language>zh-CN</language><managingEditor>17691281867@163.com (Wenzhuo Huang)</managingEditor><webMaster>17691281867@163.com (Wenzhuo Huang)</webMaster><copyright>© 2026 Wenzhuo Huang</copyright><lastBuildDate>Sat, 21 Feb 2026 09:30:00 +0800</lastBuildDate><atom:link href="https://socake.github.io/tags/embedding/index.xml" rel="self" type="application/rss+xml"/><item><title>Embedding 模型选型与优化实战：从 BGE 到 OpenAI Embedding</title><link>https://socake.github.io/posts/embedding-model-selection-guide/</link><pubDate>Sat, 21 Feb 2026 09:30:00 +0800</pubDate><author>17691281867@163.com (Wenzhuo Huang)</author><guid>https://socake.github.io/posts/embedding-model-selection-guide/</guid><description>系统对比 2026 年主流 Embedding 模型，从原理到工程实践，覆盖选型决策、缓存设计和批量优化</description><media:content xmlns:media="http://search.yahoo.com/mrss/" url="https://socake.github.io/posts/embedding-model-selection-guide/featured.jpg"/></item><item><title>大模型核心概念：工程师需要理解的 LLM 基础</title><link>https://socake.github.io/posts/llm-core-concepts/</link><pubDate>Mon, 17 Nov 2025 11:37:00 +0800</pubDate><author>17691281867@163.com (Wenzhuo Huang)</author><guid>https://socake.github.io/posts/llm-core-concepts/</guid><description>同事第一次用 GPT-4 API 写代码时问我：为什么我发了一段中文，token 消耗比英文多那么多？为什么模型有时候会一本正经地胡说八道？这篇文章把我认为工程师必须理解的 LLM 概念系统整理了一遍，不涉及 Transformer 数学，只讲对你写代码有帮助的部分。</description><media:content xmlns:media="http://search.yahoo.com/mrss/" url="https://socake.github.io/posts/llm-core-concepts/featured.jpg"/></item><item><title>RAG 系统设计与实战：检索增强生成完全指南</title><link>https://socake.github.io/posts/rag-system-design-practice/</link><pubDate>Tue, 11 Nov 2025 11:41:00 +0800</pubDate><author>17691281867@163.com (Wenzhuo Huang)</author><guid>https://socake.github.io/posts/rag-system-design-practice/</guid><description>RAG（检索增强生成）是目前企业落地 LLM 最主流的方式。本文覆盖 RAG 系统的完整设计：文档处理管线、分块策略、向量检索与关键词混合检索、Rerank 重排序、上下文压缩，以及用 RAGAS 框架评估 RAG 质量，最后分享生产环境踩坑记录。</description><media:content xmlns:media="http://search.yahoo.com/mrss/" url="https://socake.github.io/posts/rag-system-design-practice/featured.jpg"/></item></channel></rss>