<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>AI Testing on AI VOID</title><link>https://ai-blog.noorshomelab.dev/tags/ai-testing/</link><description>Recent content in AI 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/ai-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><item><title>AI-Driven Testing: Generating Tests and Validating Code</title><link>https://ai-blog.noorshomelab.dev/ai-coding-systems-2026/ai-driven-testing/</link><pubDate>Fri, 20 Mar 2026 00:00:00 +0000</pubDate><guid>https://ai-blog.noorshomelab.dev/ai-coding-systems-2026/ai-driven-testing/</guid><description>&lt;h2 id="introduction-to-ai-driven-testing"&gt;Introduction to AI-Driven Testing&lt;/h2&gt;
&lt;p&gt;Welcome back, intrepid developer! In our journey through AI coding systems, we&amp;rsquo;ve explored how these powerful tools can generate code, assist with debugging, and even help craft pull requests. But what about ensuring the quality and correctness of all that AI-generated code, or even your own human-written code? That&amp;rsquo;s where AI-driven testing comes into play, and it&amp;rsquo;s the focus of this exciting chapter!&lt;/p&gt;
&lt;p&gt;AI coding systems are rapidly evolving from mere autocomplete tools to sophisticated assistants capable of understanding context, generating complex logic, and critically, helping you validate your work. We&amp;rsquo;ll delve into how tools like GitHub Copilot and Cursor 2.6 can be leveraged to generate unit tests, integration tests, and even assist in identifying potential issues before they become bugs. This isn&amp;rsquo;t just about saving time; it&amp;rsquo;s about elevating the quality and robustness of your software.&lt;/p&gt;</description></item><item><title>AI System Evaluation and Guardrails Guide</title><link>https://ai-blog.noorshomelab.dev/ai-reliability-guide-2026/</link><pubDate>Fri, 20 Mar 2026 00:00:00 +0000</pubDate><guid>https://ai-blog.noorshomelab.dev/ai-reliability-guide-2026/</guid><description>&lt;p&gt;This comprehensive guide delves into ensuring the reliability and safety of AI systems in production. Explore essential techniques like prompt testing, hallucination detection, and robust output validation to build trustworthy AI. Discover strategies for designing effective safety filters and guardrails, complete with real-world tools and implementation advice.&lt;/p&gt;</description></item></channel></rss>