<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Measurement on Revelara</title><link>https://revelara.ai/tags/measurement/</link><description>Recent content in Measurement on Revelara</description><generator>Hugo</generator><language>en-us</language><lastBuildDate>Wed, 15 Jul 2026 08:00:00 -0700</lastBuildDate><atom:link href="https://revelara.ai/tags/measurement/index.xml" rel="self" type="application/rss+xml"/><item><title>The Counterfeit Test for AI Metrics</title><link>https://revelara.ai/blog/the-counterfeit-test-for-ai-metrics/</link><pubDate>Wed, 15 Jul 2026 08:00:00 -0700</pubDate><guid>https://revelara.ai/blog/the-counterfeit-test-for-ai-metrics/</guid><description>&lt;p&gt;We had an AI quality metric that behaved exactly the way any good metric should. It was stable across model tiers. It improved when we refined the system. It separated weaker specialist lenses from stronger ones. And it measured something tied to proprietary knowledge the base model did not have.&lt;/p&gt;
&lt;p&gt;It measured &lt;strong&gt;grounding coverage&lt;/strong&gt;: the percentage of findings that cited a valid control from our reliability catalog. Every control in the catalog traces back to real incidents and known failure patterns.&lt;/p&gt;</description></item></channel></rss>