<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Compound-Systems on Ghafoor's Personal Blog</title><link>http://ghafoorsblog.com/tags/compound-systems/</link><description>Recent content in Compound-Systems 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/compound-systems/index.xml" rel="self" type="application/rss+xml"/><item><title>Agent Usage</title><link>http://ghafoorsblog.com/courses/ibm/ai-developer-content/ai-developer-pcert/02-introduction-to-ai/03-module/002-agent-usage/</link><pubDate>Fri, 11 Jul 2025 11:35:48 +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/03-module/002-agent-usage/</guid><description>&lt;p class="lead text-primary"&gt;
This document explains the evolution from monolithic AI models to compound AI systems, demonstrating how combining models with programmatic components and external data sources enables more accurate, adaptable, and context-aware solutions for complex tasks.
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&lt;h2 id="from-monolithic-models-to-compound-ai-systems"&gt;From Monolithic Models to Compound AI Systems&lt;/h2&gt;
&lt;p&gt;Traditional AI models are limited by the data they are trained on and are difficult to adapt to new tasks or information. Adapting such models requires significant investment in data and resources. For example, a language model cannot answer personalized queries, such as vacation days available for a specific user, without access to external data.&lt;/p&gt;</description></item></channel></rss>