A comparison framework for choosing between two knowledge-base architectures: the LLM Wiki Pattern (markdown + index + LLM reader) and semantic RAG (embeddings + vector database + similarity search).
Comparison
| Dimension | LLM Wiki | Semantic RAG |
|---|---|---|
| Infrastructure | Zero (flat files) | Vector DB, embedding model |
| Cost | Free (storage only) | Per-query embedding cost + DB cost |
| Best scale | Up to ~hundreds of pages | Millions of documents |
| Accuracy | High (full-page context) | Medium (chunk context loss) |
| Setup time | Minutes | Hours to days |
| Maintenance | Manual ingest | Pipeline automation required |
Key points
- “One X user turned 383 scattered files and over 100 meeting transcripts into a compact wiki and dropped token usage by 95% when querying with Claude.” [src-013]
- The 95% reduction is relative to unstructured context dumps — the wiki compacts raw content into dense indexed pages [src-013]
- Semantic RAG wins above millions of documents where the LLM cannot hold the index in context [src-013]
- For personal knowledge bases and team wikis under ~hundreds of pages, LLM wiki delivers better answers with simpler infrastructure [src-013]
Related entities
- Andrej Karpathy — originator of the LLM wiki pattern
- Pinecone — representative vector DB for semantic RAG
- Obsidian — visual layer for the LLM wiki
Related concepts
- Karpathy LLM Wiki Pattern — detailed architecture of the LLM wiki approach
- Retrieval-Augmented Generation — the semantic RAG approach
- Token Economics — token cost profile differs significantly between the two
Source references
- [src-013] Nate Herk — “Build & Sell Claude Code Operating Systems (2+ Hour Course)” (2026-05-01)