<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Evaluations on Start AI Tools - Presented by Intent Solutions</title><link>https://startaitools.com/tags/evaluations/</link><description>Recent content in Evaluations on Start AI Tools - Presented by Intent Solutions</description><generator>Hugo</generator><language>en-US</language><copyright>Intent Solutions. All rights reserved.</copyright><lastBuildDate>Wed, 14 Oct 2026 08:00:00 -0600</lastBuildDate><atom:link href="https://startaitools.com/tags/evaluations/index.xml" rel="self" type="application/rss+xml"/><item><title>When Your Documentation Evaluation Is Driving the Cleanup, Report Gap Answers by Rank Instead of Adding Them to the Scored Set</title><link>https://startaitools.com/posts/rank-only-reporting-keeps-the-eval-honest/</link><pubDate>Wed, 14 Oct 2026 08:00:00 -0600</pubDate><guid>https://startaitools.com/posts/rank-only-reporting-keeps-the-eval-honest/</guid><description>&lt;p&gt;If you are running a search or retrieval evaluation against a documentation corpus, and the same eval is what surfaces the holes you then fill, you have a quiet training problem. The minute you add the gap-filler doc to the scored question set, the eval learns to award the new doc because it exists. Run-to-run comparability on your fixed corpus starts to drift, because each cleanup round reshapes the answer key. You lose the one number you trust: the score on a stable set of graded questions.&lt;/p&gt;</description></item></channel></rss>