<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Ai-Accuracy on Ghafoor's Personal Blog</title><link>http://ghafoorsblog.com/tags/ai-accuracy/</link><description>Recent content in Ai-Accuracy on Ghafoor's Personal Blog</description><generator>Hugo</generator><language>en</language><managingEditor>noreply@example.com (AG Sayyed)</managingEditor><webMaster>noreply@example.com (AG Sayyed)</webMaster><copyright>Copyright © 2024-2026 AG Sayyed. All Rights Reserved.</copyright><lastBuildDate>Fri, 31 Oct 2025 11:40:28 +0000</lastBuildDate><atom:link href="http://ghafoorsblog.com/tags/ai-accuracy/index.xml" rel="self" type="application/rss+xml"/><item><title>Hallucination in Large Language Models</title><link>http://ghafoorsblog.com/courses/ibm/ai-developer-content/ai-developer-pcert/02-introduction-to-ai/04-module/003-hallucination/</link><pubDate>Fri, 11 Jul 2025 15:54:06 +0000</pubDate><author>noreply@example.com (AG Sayyed)</author><guid>http://ghafoorsblog.com/courses/ibm/ai-developer-content/ai-developer-pcert/02-introduction-to-ai/04-module/003-hallucination/</guid><description>&lt;p class="lead text-primary"&gt;
This document explores hallucination in large language models (LLMs), including what it is, why it occurs, the types of hallucinations, and actionable steps to reduce fabricated or inaccurate outputs in AI-generated content.
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&lt;h2 id="introduction"&gt;Introduction&lt;/h2&gt;
&lt;p&gt;Large language models (LLMs) like ChatGPT and Bing Chat can generate fluent, coherent text on many topics, but they are also prone to hallucination—producing plausible-sounding but incorrect or fabricated information. Understanding and minimizing hallucination is essential for trustworthy AI.&lt;/p&gt;</description></item></channel></rss>