LLM wiki by Karpathy method: automation

# Automated Wiki Powered by LLM: Andrey Karpathy's Method for IT Professionals

Andrey Karpathy uses LLM to create structured wikis from raw materials: articles, scientific publications, repositories, and datasets. Materials are collected into a single folder, after which the model generates a collection of markdown files. This includes summaries, cross-references, and standalone articles on key concepts. One knowledge base contains about 100 documents totaling 400,000 words—all content generated automatically, without any manual input.

The approach eliminates the need for RAG pipelines. The LLM independently manages indexes, document summaries, and retrieves relevant snippets without additional infrastructure. For browsing, use Obsidian; for presentations, Marp.

Knowledge Base Self-Enrichment Cycle

The system runs in a closed loop: query results are fed back into the wiki. This covers answers to questions, generated charts, and slides. Enrichment happens automatically, boosting the quality of future interactions.

The LLM performs integrity checks:

• Detecting contradictions between documents.
• Filling gaps via web search.
• Suggesting topics for new articles.

This architecture lets the knowledge base evolve without outside intervention. Index files enable smooth navigation, while document descriptions speed up retrieval.

Interface and Tools

Obsidian serves as the main interface for navigating the markdown structure. Its knowledge graph support and plugins make it easy to visualize connections between concepts. Marp turns files into presentation slides while preserving formatting.

No custom infrastructure means easy deployment. The LLM agent handles everything from compilation to queries. This makes the method accessible to mid- and senior-level developers working with large data corpora.

Development Prospects

Future enhancements include synthetic data and fine-tuning. The LLM could integrate the knowledge base directly into its weights, sidestepping context window limits. This would cut latency and boost accuracy.

The current setup is a collection of scripts, but the potential for a full product is clear. Automating knowledge bases solves curation challenges for technical pros.

Key Points:

• LLM independently indexes and retrieves data without RAG.
• Feedback loop enriches the base with answers and charts.
• Health checks spot contradictions and gaps.
• Obsidian + Marp as a ready-to-go interface stack.
• Outlook: fine-tuning to internalize knowledge.

The method scales to any domain: from ML research to corporate knowledge. All you need is an LLM with agent capabilities and markdown rendering.

— Editorial Team