<?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>Prompt优化 on 硅基 AGI · 智能体学习与测评</title><link>https://guijiagi.com/tags/prompt%E4%BC%98%E5%8C%96/</link><description>Recent content in Prompt优化 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>Tue, 30 Jun 2026 11:05:00 +0800</lastBuildDate><atom:link href="https://guijiagi.com/tags/prompt%E4%BC%98%E5%8C%96/index.xml" rel="self" type="application/rss+xml"/><item><title>Prompt版本控制与A/B测试：数据驱动的Prompt优化</title><link>https://guijiagi.com/posts/prompt-version-control-ab-testing/</link><pubDate>Tue, 30 Jun 2026 11:05:00 +0800</pubDate><guid>https://guijiagi.com/posts/prompt-version-control-ab-testing/</guid><description>详细介绍如何对Prompt实施版本控制与A/B测试，建立数据驱动的Prompt优化流程，包含统计方法与工程实现</description></item><item><title>多轮对话Prompt优化策略</title><link>https://guijiagi.com/posts/multi-turn-dialogue-optimization/</link><pubDate>Sat, 27 Jun 2026 15:00:00 +0800</pubDate><guid>https://guijiagi.com/posts/multi-turn-dialogue-optimization/</guid><description>多轮对话场景下的Prompt优化策略，解决上下文管理、话题漂移和一致性挑战</description></item><item><title>DSPy 框架评测：声明式 LLM 编程</title><link>https://guijiagi.com/posts/dspyp-review/</link><pubDate>Wed, 24 Jun 2026 16:00:00 +0800</pubDate><guid>https://guijiagi.com/posts/dspyp-review/</guid><description>深入评测 DSPy 框架的 Signature/Module/Teleprompter 架构、自动 Prompt 优化能力及与 LangChain 的对比</description></item></channel></rss>