Andrej Karpathy recently shared a fascinating concept called the "LLM Wiki"—a brilliant shift from how most of us currently interact with our documents.
Right now, the standard approach is RAG (Retrieval-Augmented Generation). You upload files, ask a question, and the LLM retrieves chunks to generate an answer. It works, but there's a catch: the LLM is rediscovering knowledge from scratch every single time. Nothing compounds.
Enter the LLM Wiki approach: 🧠
Instead of raw retrieval at query time, the LLM acts as a dedicated maintainer of a persistent, structured, and interlinked markdown wiki.
Here is how it changes the game:
1️⃣ Compounding Knowledge: When you add a new source, the LLM doesn't just index it. It reads it, extracts key info, updates existing entity pages, and flags contradictions. The knowledge is compiled once and kept current.
2️⃣ Division of Labor: You don't write the wiki. You are in charge of curating sources, exploring, and asking the right questions. The LLM does the grunt work—summarizing, cross-referencing, filing, and bookkeeping.
3️⃣ The Architecture: 🔹 Raw Sources: Your immutable files (PDFs, articles).
🔹 The Wiki: The LLM-generated markdown files (the synthesis).
🔹 The Schema: The instructions that tell the LLM exactly how to structure and maintain your specific domain.
Karpathy notes: "Obsidian is the IDE; the LLM is the programmer; the wiki is the codebase." 💻
Imagine using this for deep-dive research, tracking your personal goals over years, building companion wikis for complex books, or maintaining an internal team knowledge base that actually stays up to date!
The biggest bottleneck of maintaining a knowledge base has never been the thinking—it’s the bookkeeping. And LLMs don't get bored of bookkeeping.
Have you tried using AI to actively maintain a personal wiki yet, or are you still relying on standard RAG? Let’s discuss below! 👇
AI #MachineLearning #Productivity #LLM #KnowledgeManagement #AndrejKarpathy #TechTrends #Obsidian
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