<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Generative AI on AI VOID</title><link>https://ai-blog.noorshomelab.dev/categories/generative-ai/</link><description>Recent content in Generative AI 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/categories/generative-ai/index.xml" rel="self" type="application/rss+xml"/><item><title>Detecting &amp;amp; Mitigating Hallucinations in Generative AI</title><link>https://ai-blog.noorshomelab.dev/ai-reliability-guide-2026/generative-ai-hallucination-detection-mitigation/</link><pubDate>Fri, 20 Mar 2026 00:00:00 +0000</pubDate><guid>https://ai-blog.noorshomelab.dev/ai-reliability-guide-2026/generative-ai-hallucination-detection-mitigation/</guid><description>&lt;h2 id="detecting--mitigating-hallucinations-in-generative-ai"&gt;Detecting &amp;amp; Mitigating Hallucinations in Generative AI&lt;/h2&gt;
&lt;p&gt;Welcome back, AI explorers! In our journey through building reliable AI systems, we&amp;rsquo;ve explored foundational evaluation techniques and robust prompt testing. Now, we&amp;rsquo;re diving into one of the most intriguing and challenging aspects of generative AI: &lt;strong&gt;hallucinations&lt;/strong&gt;.&lt;/p&gt;
&lt;p&gt;Generative AI models, especially Large Language Models (LLMs), are incredible at creating human-like text, images, and more. But sometimes, they get a little &lt;em&gt;too&lt;/em&gt; creative, generating information that sounds perfectly plausible but is factually incorrect, nonsensical, or entirely made up. This phenomenon is known as &lt;strong&gt;AI hallucination&lt;/strong&gt;.&lt;/p&gt;</description></item></channel></rss>