<?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 AI For Human Expertise</title><link>https://aiforhumanexpertise.com/tags/retrieval-augmented-generation/</link><description>Recent content in Retrieval-Augmented-Generation on AI For Human Expertise</description><generator>Hugo</generator><language>en-gb</language><lastBuildDate>Mon, 29 Jun 2026 00:00:00 +0000</lastBuildDate><atom:link href="https://aiforhumanexpertise.com/tags/retrieval-augmented-generation/index.xml" rel="self" type="application/rss+xml"/><item><title>From RAG To KAG: The Structured-Knowledge Upgrade</title><link>https://aiforhumanexpertise.com/blog/from-rag-to-kag-the-structured-knowledge-upgrade/</link><pubDate>Mon, 29 Jun 2026 00:00:00 +0000</pubDate><guid>https://aiforhumanexpertise.com/blog/from-rag-to-kag-the-structured-knowledge-upgrade/</guid><description>&lt;h2 id="rag-solved-one-problem-and-created-another"&gt;RAG Solved One Problem And Created Another&lt;/h2&gt;
&lt;p&gt;Retrieval augmented generation became the default architecture for grounding language model outputs in 2023 and 2024, and for good reason. It addressed the most obvious failure mode of out-of-the-box large language models, which was producing fluent text on topics the model did not actually have current or correct information about. RAG pulls relevant documents into the model&amp;rsquo;s context at inference time. The model can cite them. Hallucinations drop. Useful systems get built.&lt;/p&gt;</description></item></channel></rss>