<?xml version="1.0" encoding="utf-8" standalone="yes"?>
<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom" xmlns:content="http://purl.org/rss/1.0/modules/content/">
  <channel>
    <title>Retrieval-Augmented Generation (RAG) on Beneath Abstraction</title>
    <link>https://www.beneathabstraction.com/tags/retrieval-augmented-generation-rag/</link>
    <description>Recent content in Retrieval-Augmented Generation (RAG) on Beneath Abstraction</description>
    <image>
      <title>Beneath Abstraction</title>
      <url>https://www.beneathabstraction.com/images/logo.png</url>
      <link>https://www.beneathabstraction.com/images/logo.png</link>
    </image>
    <generator>Hugo -- 0.157.0</generator>
    <language>en</language>
    <lastBuildDate>Fri, 21 Jun 2024 18:50:46 +1000</lastBuildDate>
    <atom:link href="https://www.beneathabstraction.com/tags/retrieval-augmented-generation-rag/index.xml" rel="self" type="application/rss+xml" />
    <item>
      <title>Enhancing Language Models Using RAG Architecture in Azure AI Studio</title>
      <link>https://www.beneathabstraction.com/post/rag-using-azure-ai-studio/</link>
      <pubDate>Fri, 21 Jun 2024 18:50:46 +1000</pubDate>
      <guid>https://www.beneathabstraction.com/post/rag-using-azure-ai-studio/</guid>
      <description>This guide will walk you through the process of enhancing language models using RAG architecture in Azure AI Studio. Retrieval-Augmented Generation (RAG) enhances Large Language Model (LLM) capabilities, like those of GPTs, by integrating an information retrieval system. This addition grounds data and controls the context for the LLM’s response generation.</description>
    </item>
  </channel>
</rss>
