<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Context Prioritization on AI VOID</title><link>https://ai-blog.noorshomelab.dev/tags/context-prioritization/</link><description>Recent content in Context Prioritization 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/context-prioritization/index.xml" rel="self" type="application/rss+xml"/><item><title>Dynamic Context: Prioritization &amp;amp; Sliding Windows for Agents</title><link>https://ai-blog.noorshomelab.dev/context-engineering-guide/dynamic-context-prioritization-sliding-windows/</link><pubDate>Fri, 20 Mar 2026 00:00:00 +0000</pubDate><guid>https://ai-blog.noorshomelab.dev/context-engineering-guide/dynamic-context-prioritization-sliding-windows/</guid><description>&lt;h2 id="introduction-to-dynamic-context"&gt;Introduction to Dynamic Context&lt;/h2&gt;
&lt;p&gt;Welcome back, fellow AI engineers! In our previous chapters, we laid the groundwork for effective context engineering. We learned how to design context, reduce its size through summarization and filtering, compress it for efficiency, and chunk it into manageable pieces. These foundational techniques are crucial, but they primarily deal with &lt;em&gt;static&lt;/em&gt; context – information that&amp;rsquo;s prepared once and then fed to the LLM.&lt;/p&gt;
&lt;p&gt;But what about long-running conversations, persistent agents, or applications that need to maintain a &amp;ldquo;memory&amp;rdquo; over extended periods? The fixed context window of LLMs, while growing, still presents a significant challenge. This is where &lt;strong&gt;dynamic context management&lt;/strong&gt; comes into play.&lt;/p&gt;</description></item></channel></rss>