<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Embedding on Portfolio</title><link>https://tatchapero.github.io/Portfolio/tags/embedding/</link><description>Recent content in Embedding on Portfolio</description><generator>Hugo -- gohugo.io</generator><language>en</language><managingEditor>t.atchapero@gmail.com (Thomas Atchapero)</managingEditor><webMaster>t.atchapero@gmail.com (Thomas Atchapero)</webMaster><copyright>© 2026 Thomas Atchapero</copyright><lastBuildDate>Mon, 13 Apr 2026 00:00:00 +0000</lastBuildDate><atom:link href="https://tatchapero.github.io/Portfolio/tags/embedding/index.xml" rel="self" type="application/rss+xml"/><item><title>RAG</title><link>https://tatchapero.github.io/Portfolio/projects/rag/</link><pubDate>Mon, 13 Apr 2026 00:00:00 +0000</pubDate><author>t.atchapero@gmail.com (Thomas Atchapero)</author><guid>https://tatchapero.github.io/Portfolio/projects/rag/</guid><description>RAG (Retrieval Augmented Generation) is an AI technique that improves answers by combining information retrieval from external sources with language model generation. It helps overcome limitations of standard models by providing up-to-date, accurate, and context-specific responses.</description><media:content xmlns:media="http://search.yahoo.com/mrss/" url="https://tatchapero.github.io/Portfolio/projects/rag/cover.png"/></item></channel></rss>