<rss xmlns:atom="http://www.w3.org/2005/Atom" version="2.0"><channel><title>RAG - Tag - Jorgen Bergstrom</title><link>https://bergstrom.org/tags/rag/</link><description>RAG - Tag - Jorgen Bergstrom</description><generator>Hugo -- gohugo.io</generator><language>en-us</language><copyright>Jorgen Bergstrom</copyright><lastBuildDate>Thu, 05 Nov 2026 00:00:00 -0500</lastBuildDate><atom:link href="https://bergstrom.org/tags/rag/" rel="self" type="application/rss+xml"/><item><title>Giving a Local LLM Your Own PDFs: Context Stuffing vs RAG</title><link>https://bergstrom.org/posts/rag_vs_full_context/</link><pubDate>Thu, 05 Nov 2026 00:00:00 -0500</pubDate><author>Jorgen Bergstrom</author><guid>https://bergstrom.org/posts/rag_vs_full_context/</guid><description>Introduction A local LLM knows nothing about your files. Give it a spec sheet, a manual, or a set of research papers and it will answer confidently from whatever it memorized during training — even when that answer is wrong for your document. The fix is to put the relevant text in front of it. There are three common ways to do that, sitting on a spectrum of how much text you inject:</description></item></channel></rss>