<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Advanced RAG on AI VOID</title><link>https://ai-blog.noorshomelab.dev/tags/advanced-rag/</link><description>Recent content in Advanced RAG 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/advanced-rag/index.xml" rel="self" type="application/rss+xml"/><item><title>Crafting Coherent Context: Moving Beyond Simple Chunking with Advanced Context Assembly</title><link>https://ai-blog.noorshomelab.dev/rag-2-0-guide-2026/advanced-context-assembly/</link><pubDate>Fri, 20 Mar 2026 00:00:00 +0000</pubDate><guid>https://ai-blog.noorshomelab.dev/rag-2-0-guide-2026/advanced-context-assembly/</guid><description>&lt;h2 id="introduction-the-quest-for-perfect-context"&gt;Introduction: The Quest for Perfect Context&lt;/h2&gt;
&lt;p&gt;Welcome back, fellow RAG adventurers! In our previous chapters, we laid the groundwork for Retrieval-Augmented Generation (RAG) by understanding its core components and the importance of effective retrieval. We briefly touched upon how breaking down documents into smaller pieces, or &amp;ldquo;chunks,&amp;rdquo; is crucial for feeding relevant information to our Large Language Models (LLMs).&lt;/p&gt;
&lt;p&gt;But here&amp;rsquo;s a little secret: while simple chunking is a good starting point, it&amp;rsquo;s often the Achilles&amp;rsquo; heel of basic RAG systems. Why? Because the way we prepare and present context to our LLM profoundly impacts the quality, accuracy, and relevance of its generated answers. If the context is fragmented, incomplete, or distorted, even the smartest LLM will struggle to provide a truly insightful response.&lt;/p&gt;</description></item></channel></rss>