Building my own LLM-Wiki Research Team

2026-06-21 · Source: Towards AI - Medium · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Software Development & Engineering · Depth: Intermediate, quick

Summary

Inspired by Andrej Karpathy's Github GiST, the author details the concept of an LLM-Wiki research team, a personal knowledge base designed to compile notes into a persistent, compounding markdown wiki using an LLM agent. This approach aims to overcome the limitations of vanilla RAG by treating notes like code. The author's motivation stemmed from a desire to replace Notion in their workflow, find a personal project to test agentic frameworks, and develop a more efficient method for thesis research, note-taking, and source referencing. The article highlights that Karpathy's original implementation, which suggests using a code editor (like Claude Code, Codex), is inherently developer-centric. Many developers, researchers, and writers are likely adopting similar iterations of this LLM-Wiki approach for knowledge organization.

Key takeaway

For researchers or students managing extensive knowledge bases, consider adopting an LLM-Wiki approach to transform your note-taking and information retrieval. Instead of repeatedly querying raw data, you should compile your insights into a persistent, agent-driven markdown wiki. This method can significantly enhance your research efficiency, streamline thesis preparation, and reduce reliance on traditional tools like Notion, offering a more dynamic and compounding knowledge system.

Key insights

Treat notes like code and compile them into a persistent, compounding markdown wiki using an LLM Agent to avoid rediscovering insights.

Principles

• Compile notes once into a persistent wiki.
• Use LLM agents for knowledge organization.
• Avoid vanilla RAG for insight rediscovery.

Method

Compile notes into a persistent markdown wiki using an LLM agent, treating them like code for efficient knowledge retrieval, potentially leveraging a code editor for implementation.

In practice

• Replace Notion for personal study workflows.
• Upskill with agentic framework projects.
• Streamline thesis research and note-editing.

Topics

• LLM Agents
• Knowledge Management
• Personal Wikis
• Research Workflow
• Markdown
• Retrieval-Augmented Generation

Best for: AI Engineer, AI Student, Software Engineer

Related on AIssential

• LLM Wiki Revolution: How Andrej Karpathy’s Idea is Changing AI — LLM-powered wikis build persistent, interconnected knowledge at ingestion, overcoming RAG's query-time processing limitations.
• Build a Powerful AI Research Pipeline with LM Studio and NotebookLM — Combining local LLMs with source-grounded knowledge tools enhances research and content creation workflows.
• ScholarQuest: A Taxonomy-Guided Benchmark for Agentic Academic Paper Search in Open Literature Environments — ScholarQuest benchmark reveals LLM-based search agents significantly underperform in academic literature exploration, despite outperforming baselines.
• Diagnosing Knowledge Gaps in LLM Tool Use: An Agentic Benchmark for Novel API Acquisition — LLM tool use with novel APIs requires specific knowledge components, with usage examples being critical, and retrieval/tuning playing complementary roles.
• Give Your AI Unlimited Updated Context — An LLM-maintained personal wiki creates a persistent, compounding knowledge artifact, eliminating repetitive context setup.
• Leveraging LLMs in your Obsidian Notes — LLMs can transform note-taking by enabling conceptual search and conversational interaction with personal knowledge bases.

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Editorial summary, takeaway, and curation by AIssential. Original article published by Towards AI - Medium.