<?xml version="1.0" encoding="UTF-8"?><rss xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:content="http://purl.org/rss/1.0/modules/content/" xmlns:atom="http://www.w3.org/2005/Atom" version="2.0"><channel><title><![CDATA[What is a RAG in AI?]]></title><description><![CDATA[<p>If you are trying to understand what is a rag in ai, then know that RAG stands for Retrieval Augmented Generation. It enables an AI system to retrieve relevant data from an external source of knowledge and generate a response using it. While the conventional AI model uses information that has been acquired during training time, the AI <a href="https://dataqix.com/retrieval-augmented-generation/" rel="nofollow">RAG model</a> can use recent or organizational specific information available through a connection to the database or documents. For instance, the organization can use the RAG model to respond to any employee queries by referring to their internal policies and documents. The process involves searching for relevant content first and providing this data to the language model.</p>
]]></description><link>https://www.callcentersindia.co.in/topic/12366/what-is-a-rag-in-ai</link><generator>RSS for Node</generator><lastBuildDate>Mon, 17 Aug 2026 12:59:57 GMT</lastBuildDate><atom:link href="https://www.callcentersindia.co.in/topic/12366.rss" rel="self" type="application/rss+xml"/><pubDate>Mon, 17 Aug 2026 06:55:30 GMT</pubDate><ttl>60</ttl><item><title><![CDATA[Reply to What is a RAG in AI? on Invalid Date]]></title><description><![CDATA[<p>If you are trying to understand what is a rag in ai, then know that RAG stands for Retrieval Augmented Generation. It enables an AI system to retrieve relevant data from an external source of knowledge and generate a response using it. While the conventional AI model uses information that has been acquired during training time, the AI <a href="https://dataqix.com/retrieval-augmented-generation/" rel="nofollow">RAG model</a> can use recent or organizational specific information available through a connection to the database or documents. For instance, the organization can use the RAG model to respond to any employee queries by referring to their internal policies and documents. The process involves searching for relevant content first and providing this data to the language model.</p>
]]></description><link>https://www.callcentersindia.co.in/post/14597</link><guid isPermaLink="true">https://www.callcentersindia.co.in/post/14597</guid><dc:creator><![CDATA[data qix]]></dc:creator><pubDate>Invalid Date</pubDate></item></channel></rss>