Wiki vs RAG
Wiki vs RAG
Overview
Both approaches let you query a large document collection. They differ fundamentally in when synthesis happens.
Comparison
| Dimension | LLM Wiki | Semantic RAG |
|---|---|---|
| How knowledge is stored | Pre-compiled markdown pages with cross-references already built | Raw chunks in a vector database |
| Finding answers | Read index → follow links → synthesize | Embed query → similarity search → assemble |
| Query cost | Low — synthesis already done | Higher — re-derives on every query |
| Infrastructure | Just markdown files | Embedding model + vector DB + chunking pipeline |
| Maintenance | Run a lint pass | Re-embed when content changes |
| Scale limit | ~hundreds of pages (index file navigation) | Millions of documents |
| Setup time | 5 minutes | Hours to days |
| Contradiction detection | Built in — LLM flags on ingest | Manual |
Verdict
Under 1000 pages → LLM Wiki. The index file is sufficient for navigation, token cost is low, setup is minimal, and the pre-compiled synthesis means every query benefits from everything ever read.
Over 100K pages → RAG. The index file becomes too large to read, and embedding-based retrieval becomes more efficient than full-index scanning.
The sweet spot: run the wiki pattern for active research (where things are being added, synthesized, and connected), then export to a vector store if the collection grows beyond the index threshold.
(Source: LLM Wiki Pattern, Compounding Knowledge)