<?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>ICL on 硅基 AGI · 智能体学习与测评</title><link>https://guijiagi.com/tags/icl/</link><description>Recent content in ICL on 硅基 AGI · 智能体学习与测评</description><generator>Hugo</generator><language>zh-cn</language><copyright>本站内容采用 CC BY-NC-SA 4.0 国际许可协议授权</copyright><lastBuildDate>Sun, 28 Jun 2026 10:40:00 +0800</lastBuildDate><atom:link href="https://guijiagi.com/tags/icl/index.xml" rel="self" type="application/rss+xml"/><item><title>Few-Shot Prompt 优化：示例选择的算法化方法</title><link>https://guijiagi.com/posts/few-shot-prompt-optimization/</link><pubDate>Sun, 28 Jun 2026 10:40:00 +0800</pubDate><guid>https://guijiagi.com/posts/few-shot-prompt-optimization/</guid><description>从人工选择到算法化选择：Few-Shot Prompt示例选择的最新方法与实现</description></item><item><title>Few-shot Prompting 指南：示例选择的科学与艺术</title><link>https://guijiagi.com/posts/few-shot-prompting-guide/</link><pubDate>Thu, 25 Jun 2026 10:00:00 +0800</pubDate><guid>https://guijiagi.com/posts/few-shot-prompting-guide/</guid><description>深入解析 In-Context Learning 原理、示例数量与选择策略对 LLM 输出质量的影响</description></item></channel></rss>