<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Graph Databases on AI VOID</title><link>https://ai-blog.noorshomelab.dev/tags/graph-databases/</link><description>Recent content in Graph Databases 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/graph-databases/index.xml" rel="self" type="application/rss+xml"/><item><title>RAG 2.0: From Basic to Advanced Retrieval-Augmented Generation</title><link>https://ai-blog.noorshomelab.dev/guides/rag-2-0-advanced-guide/</link><pubDate>Fri, 20 Mar 2026 00:00:00 +0000</pubDate><guid>https://ai-blog.noorshomelab.dev/guides/rag-2-0-advanced-guide/</guid><description>&lt;h2 id="welcome-to-modern-rag-building-intelligent-ai-systems"&gt;Welcome to Modern RAG: Building Intelligent AI Systems&lt;/h2&gt;
&lt;p&gt;Hello there! If you&amp;rsquo;re working with Large Language Models (LLMs), you&amp;rsquo;ve likely encountered Retrieval-Augmented Generation (RAG). It&amp;rsquo;s a powerful technique that helps LLMs provide more accurate and up-to-date answers by giving them access to external knowledge. But as you might have noticed, basic RAG can sometimes fall short, especially with complex questions or when dealing with vast, interconnected information.&lt;/p&gt;
&lt;p&gt;That&amp;rsquo;s where &lt;strong&gt;RAG 2.0&lt;/strong&gt; comes in. Think of it as an evolution, moving beyond simple document retrieval to a more intelligent, adaptive, and highly accurate way of preparing context for your LLMs. This guide will walk you through the essential techniques and best practices to build RAG systems that truly understand and respond to intricate queries.&lt;/p&gt;</description></item></channel></rss>