<?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>优化 on 硅基 AGI · 智能体学习与测评</title><link>https://guijiagi.com/tags/%E4%BC%98%E5%8C%96/</link><description>Recent content in 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>Sun, 28 Jun 2026 10:40:00 +0800</lastBuildDate><atom:link href="https://guijiagi.com/tags/%E4%BC%98%E5%8C%96/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>KV Cache优化策略详解</title><link>https://guijiagi.com/posts/kv-cache-optimization/</link><pubDate>Sat, 27 Jun 2026 15:00:00 +0800</pubDate><guid>https://guijiagi.com/posts/kv-cache-optimization/</guid><description>KV Cache优化策略详解</description></item><item><title>Prompt压缩技术</title><link>https://guijiagi.com/posts/prompt-compression/</link><pubDate>Sat, 27 Jun 2026 15:00:00 +0800</pubDate><guid>https://guijiagi.com/posts/prompt-compression/</guid><description>Prompt压缩技术</description></item><item><title>RAG效果评估与优化闭环</title><link>https://guijiagi.com/posts/rag-eval-optimization/</link><pubDate>Sat, 27 Jun 2026 15:00:00 +0800</pubDate><guid>https://guijiagi.com/posts/rag-eval-optimization/</guid><description>RAG效果评估与优化闭环</description></item><item><title>大模型推理加速技术全景</title><link>https://guijiagi.com/posts/llm-inference-acceleration-survey/</link><pubDate>Sat, 27 Jun 2026 15:00:00 +0800</pubDate><guid>https://guijiagi.com/posts/llm-inference-acceleration-survey/</guid><description>大模型推理加速技术全景</description></item><item><title>多轮对话Prompt优化策略</title><link>https://guijiagi.com/posts/multiturn-prompt-optimization/</link><pubDate>Sat, 27 Jun 2026 15:00:00 +0800</pubDate><guid>https://guijiagi.com/posts/multiturn-prompt-optimization/</guid><description>多轮对话Prompt优化策略</description></item><item><title>Prompt 迭代优化：从经验到工程化</title><link>https://guijiagi.com/posts/agent-prompt-iteration/</link><pubDate>Fri, 26 Jun 2026 14:00:00 +0800</pubDate><guid>https://guijiagi.com/posts/agent-prompt-iteration/</guid><description>将 Prompt 优化从手工试错提升为系统工程化流程，涵盖版本管理、A/B 测试、评估闭环与自动化迭代</description></item><item><title>Flash Attention 3 原理：GPU 内存层次的最优利用</title><link>https://guijiagi.com/posts/flash-attention-3/</link><pubDate>Thu, 25 Jun 2026 12:00:00 +0800</pubDate><guid>https://guijiagi.com/posts/flash-attention-3/</guid><description>深度解析 Flash Attention 3 如何通过异步执行、warp 专用化和 FP8 支持，在 H100 GPU 上实现比 Flash Attention 2 快 1.5-2 倍的性能。</description></item><item><title>LLM 成本优化实战：10 种降低 API 费用的方法</title><link>https://guijiagi.com/posts/llm-cost-optimization/</link><pubDate>Thu, 25 Jun 2026 12:00:00 +0800</pubDate><guid>https://guijiagi.com/posts/llm-cost-optimization/</guid><description>从模型选择、Prompt 精简、缓存策略到流量路由，系统介绍 10 种可落地的 LLM API 成本优化方法，附带代码示例与量化对比。</description></item><item><title>AI 成本优化策略：从 Token 到基础设施的全链路省钱</title><link>https://guijiagi.com/posts/ai-cost-optimization-strategy/</link><pubDate>Thu, 25 Jun 2026 10:00:00 +0800</pubDate><guid>https://guijiagi.com/posts/ai-cost-optimization-strategy/</guid><description>系统性分析 LLM 应用的成本结构，涵盖 Token 优化、模型路由、缓存策略、Batch API 到基础设施层的全链路降本实践</description></item><item><title>边缘 AI 架构设计：在手机和 IoT 上运行 LLM</title><link>https://guijiagi.com/posts/edge-ai-architecture/</link><pubDate>Wed, 24 Jun 2026 16:00:00 +0800</pubDate><guid>https://guijiagi.com/posts/edge-ai-architecture/</guid><description>探讨在手机和 IoT 设备上运行 LLM 的架构设计，涵盖模型压缩、端侧推理引擎、NPU 加速和混合云-端架构</description></item><item><title>元提示技术：用 LLM 优化 LLM 的 Prompt</title><link>https://guijiagi.com/posts/meta-prompting-guide/</link><pubDate>Wed, 24 Jun 2026 16:00:00 +0800</pubDate><guid>https://guijiagi.com/posts/meta-prompting-guide/</guid><description>系统介绍 Meta-Prompting 技术，让 LLM 自动优化提示词，从 APE 到 OPRO 到自动进化</description></item></channel></rss>