<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Metrics on Yuan's Blog</title><link>https://liyuan.org/tags/metrics/</link><description>Recent content in Metrics on Yuan's Blog</description><generator>Hugo</generator><language>en-us</language><lastBuildDate>Tue, 05 May 2026 00:00:00 +0000</lastBuildDate><atom:link href="https://liyuan.org/tags/metrics/index.xml" rel="self" type="application/rss+xml"/><item><title>Picking Evaluation Metrics for a RAG Agent — Notes from the Trenches</title><link>https://liyuan.org/posts/ai/rag-eval-metrics-selection/</link><pubDate>Tue, 05 May 2026 00:00:00 +0000</pubDate><guid>https://liyuan.org/posts/ai/rag-eval-metrics-selection/</guid><description>This article outlines a pragmatic, tiered approach to evaluating Retrieval-Augmented Generation (RAG) agents, specifically within the context of complex financial document analysis (FinanceBench). The author argues that effective evaluation is not about maximizing the number of metrics, but about selecting signals that provide clear, actionable insights at different stages of the development lifecycle.</description></item></channel></rss>