# AI Wiki - PKM - LLM Wiki An LLM Wiki is a pattern for persistent, compounding knowledge bases maintained entirely by LLMs. The human discovers sources and asks strategic questions; the LLM handles all bookkeeping: summaries, cross-references, contradiction resolution, and index maintenance. The pattern was proposed by Andrej Karpathy in April 2026. ## The Core Insight LLMs excel at mechanical bookkeeping that humans reliably abandon. Most knowledge bases die not from lack of input but from lack of maintenance. Cross-references go stale, indexes become incomplete, contradictions accumulate unnoticed, and new material is never integrated with existing content. LLMs eliminate this failure mode by performing maintenance as a side effect of every operation. ## Three-Layer Architecture ### Layer 1: Raw Sources User-curated, immutable documents: articles, papers, repos, data. The LLM reads this layer but never modifies it. These are the primary sources that ground the wiki in reality. ### Layer 2: The Wiki LLM-generated and LLM-maintained Markdown files: entity pages, concept pages, summaries, and interconnected analyses. The LLM owns this layer entirely. It creates, updates, cross-links, and maintains articles as new sources are ingested and queries are answered. ### Layer 3: The Schema A configuration document (e.g., CLAUDE.md, AGENTS.md) defining structure, conventions, and workflows. This is [[AI Wiki - PKM - Context-as-Code]] applied to knowledge base maintenance. The schema tells the LLM how to organize, name, tag, and cross-reference articles. ## Core Operations **Ingest** — When a new source is added, the LLM extracts key takeaways, writes or updates wiki articles, creates cross-references to existing articles, and appends to the operation log. **Query** — The LLM searches relevant articles, synthesizes an answer with citations, and optionally promotes valuable query outputs into new wiki pages. **Lint** — A health-check operation that scans for contradictions, stale claims, orphan articles, missing cross-references, and index inconsistencies. Lint repairs what it finds. ## Structural Files Every LLM Wiki has two mandatory structural files: - **Index** — A content-organized catalog with one-line summaries for each article. Updated on every ingest, explore, or lint operation. - **Log** — A chronological, append-only operation record with parseable prefixes. Provides full audit trail. ## Why File-Based Beats RAG At scale (~100 articles / ~400K words), a well-maintained wiki with auto-maintained indexes outperforms RAG for question-answering. The LLM navigates file structure directly — following cross-references and reading targeted articles — rather than relying on embedding-based retrieval that can miss nuance and context. The Farzapedia variant (by Farza) demonstrated this with 2,500 diary entries producing 400 articles with backlinks. His key distinction: the wiki is built for the agent, not the human. File-system structure with backlinks is more easily crawlable than vector similarity search. ## LLM Wiki and Compounding Knowledge LLM Wikis are a concrete implementation of [[AI Wiki - PKM - Compounding Knowledge]]. Every ingested source triggers updates across multiple existing articles. Every query can generate new articles or improve existing ones. The wiki becomes a compounding artifact where the act of using it makes it more valuable. This is also how [[AI Wiki - PKM - Agentic Knowledge Management]] manifests in practice: the AI agent does not just answer questions — it builds and maintains the knowledge infrastructure. ## Key Points - LLMs handle the bookkeeping humans reliably abandon: cross-references, indexes, contradiction detection - Three layers: raw sources (immutable), the wiki (LLM-maintained), the schema (conventions) - Core operations: ingest, query, lint - File-based navigation outperforms RAG at scale for structured knowledge - The wiki compounds in value with every operation ## Open Questions - How to handle conflicting information across sources? - What is the optimal article granularity for LLM consumption? - Can multiple humans curate a shared LLM Wiki effectively? ## References - Andrej Karpathy, "LLM Wiki" (April 2026) - Farza, Farzapedia implementation - Vault: LLM Wiki, Compounding Knowledge ## Related - [[AI Wiki - PKM - Agentic Knowledge Management]] - [[AI Wiki - PKM - Compounding Knowledge]] - [[AI Wiki - PKM - Context-as-Code]] - [[AI Wiki - PKM - AI Skills in PKM]] - [[AI Wiki - PKM - Knowledge-Context Pipeline]] - [[AI Wiki - PKM - RAG for Personal Knowledge]] - [[AI Wiki - PKM - Plain Text and Interoperability]]