<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Regression Testing on AI VOID</title><link>https://ai-blog.noorshomelab.dev/tags/regression-testing/</link><description>Recent content in Regression Testing on AI VOID</description><generator>Hugo</generator><language>en</language><lastBuildDate>Fri, 20 Mar 2026 00:00:00 +0000</lastBuildDate><atom:link href="https://ai-blog.noorshomelab.dev/tags/regression-testing/index.xml" rel="self" type="application/rss+xml"/><item><title>Regression Testing for AI: Preventing Unintended Consequences</title><link>https://ai-blog.noorshomelab.dev/ai-reliability-guide-2026/ai-regression-testing-prevent-consequences/</link><pubDate>Fri, 20 Mar 2026 00:00:00 +0000</pubDate><guid>https://ai-blog.noorshomelab.dev/ai-reliability-guide-2026/ai-regression-testing-prevent-consequences/</guid><description>&lt;h2 id="introduction-guarding-against-ai-regression"&gt;Introduction: Guarding Against AI Regression&lt;/h2&gt;
&lt;p&gt;Welcome back, future AI reliability expert! In our previous chapters, we laid the groundwork for understanding AI evaluation and explored the crucial art of prompt testing. We learned how to carefully craft and validate inputs to our AI systems. But what happens &lt;em&gt;after&lt;/em&gt; we&amp;rsquo;ve deployed our AI? Or when we make a small change to the model, the data pipeline, or even a single prompt? How do we ensure that our shiny new improvements don&amp;rsquo;t accidentally break something that was working perfectly before?&lt;/p&gt;</description></item></channel></rss>