<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Retrieval Augmented Generation on The Coders Blog</title><link>https://thecodersblog.com/tag/retrieval-augmented-generation/</link><description>Recent content in Retrieval Augmented Generation on The Coders Blog</description><generator>Hugo</generator><language>en-us</language><lastBuildDate>Wed, 06 May 2026 22:22:02 +0000</lastBuildDate><atom:link href="https://thecodersblog.com/tag/retrieval-augmented-generation/index.xml" rel="self" type="application/rss+xml"/><item><title>Building with Gemini Embedding 2: Agentic Multimodal RAG</title><link>https://thecodersblog.com/gemini-embedding-2-for-multimodal-rag-2026/</link><pubDate>Wed, 06 May 2026 22:22:02 +0000</pubDate><guid>https://thecodersblog.com/gemini-embedding-2-for-multimodal-rag-2026/</guid><description>&lt;p&gt;Forget stitching together disparate models for text, image, and audio. The era of fragmented multimodal AI is over, thanks to Gemini Embedding 2. If you&amp;rsquo;re building retrieval-augmented generation (RAG) systems that need to truly &lt;em&gt;understand&lt;/em&gt; the world, not just read it, this is the game-changer you&amp;rsquo;ve been waiting for.&lt;/p&gt;
&lt;h2 id="the-problem-data-is-messy-ai-needs-to-be-unified"&gt;The Problem: Data is Messy, AI Needs to be Unified&lt;/h2&gt;
&lt;p&gt;Traditional RAG pipelines excel at text. But what happens when your knowledge base includes product manuals with diagrams, video tutorials explaining complex procedures, or audio recordings of customer feedback? Historically, this meant separate embedding models, complex feature extraction pipelines, and a constant struggle to find relevant information across different modalities. The result? Latency, reduced accuracy, and a development nightmare.&lt;/p&gt;</description></item></channel></rss>