<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Llm-Wiki on AI For Human Expertise</title><link>https://aiforhumanexpertise.com/tags/llm-wiki/</link><description>Recent content in Llm-Wiki on AI For Human Expertise</description><generator>Hugo</generator><language>en-gb</language><lastBuildDate>Sun, 05 Apr 2026 00:00:00 +0000</lastBuildDate><atom:link href="https://aiforhumanexpertise.com/tags/llm-wiki/index.xml" rel="self" type="application/rss+xml"/><item><title>The LLM Wiki: A Pattern for Smarter Organisational Knowledge Bases</title><link>https://aiforhumanexpertise.com/blog/llm-wiki-smarter-knowledge-bases/</link><pubDate>Sun, 05 Apr 2026 00:00:00 +0000</pubDate><guid>https://aiforhumanexpertise.com/blog/llm-wiki-smarter-knowledge-bases/</guid><description>&lt;h2 id="a-better-pattern-for-organisational-knowledge"&gt;A Better Pattern for Organisational Knowledge&lt;/h2&gt;
&lt;p&gt;Andrej Karpathy - the AI researcher known for his work at OpenAI and Tesla - recently shared a pattern he calls the &amp;ldquo;&lt;a href="https://gist.github.com/karpathy/442a6bf555914893e9891c11519de94f"&gt;LLM Wiki&lt;/a&gt;.&amp;rdquo; It addresses a fundamental limitation in how most organisations use large language models for knowledge work today.&lt;/p&gt;
&lt;p&gt;The standard approach is retrieval-augmented generation (RAG): upload documents, the AI retrieves relevant chunks at query time, and generates an answer. It powers tools like ChatGPT file uploads and most enterprise search products. It works - but nothing accumulates. Every question forces the AI to rediscover knowledge from scratch, piecing together fragments across documents each time.&lt;/p&gt;</description></item></channel></rss>