23bfd15b19
Graph topology (Metatron's Cube pattern — 1 center + 12 outer nodes): - index: now links to ALL 12 other nodes (complete hub) - Inner ring cycle: hot→log→overview→dashboard→concepts/_index→entities/_index - Added: hot↔WikiMap, log↔sources/_index, dashboard↔concepts/_index - Added: entities/_index↔hot, entities/_index↔LLM Wiki Pattern - Added: sources/_index↔log, sources/_index↔entities/_index - Outer ring: concepts connected in triangle + Karpathy/sources cross-linked - Added: dashboard↔Compounding, entities/_index↔LLM Wiki Pattern graph.json physics for geometric arrangement: - repelStrength: 80 (strong push-apart for uniform spacing) - linkStrength: 3.0 (locks ring geometry) - linkDistance: 80 (tighter rings) - centerStrength: 0.25 (moderate center pull) - nodeSizeMultiplier: 2.0 (hub nodes visually dominant) - Added colors: questions=yellow, comparisons=red, nav=teal Phantom links removed from Hot Cache.md: - Removed [[Page A]], [[Page B]], [[New Page 1]], [[Existing Page]] New module pages: - wiki/questions/How does the LLM Wiki pattern work.md - wiki/comparisons/Wiki vs RAG.md - Adds questions/ and comparisons/ domains to the graph (yellow + red nodes)
57 lines
2.1 KiB
Markdown
57 lines
2.1 KiB
Markdown
---
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type: comparison
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title: "Wiki vs RAG"
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subjects:
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- "[[LLM Wiki Pattern]]"
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- "RAG (Retrieval-Augmented Generation)"
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dimensions:
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- "How knowledge is stored"
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- "Query cost"
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- "Infrastructure"
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- "Maintenance"
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- "Scale limit"
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verdict: "Wiki wins at <1000 pages. RAG wins at enterprise scale."
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created: 2026-04-07
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updated: 2026-04-07
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tags:
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- comparison
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- llm-wiki
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- knowledge-management
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status: mature
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related:
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- "[[LLM Wiki Pattern]]"
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- "[[Compounding Knowledge]]"
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- "[[index]]"
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- "[[How does the LLM Wiki pattern work]]"
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sources: []
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---
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# Wiki vs RAG
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## Overview
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Both approaches let you query a large document collection. They differ fundamentally in when synthesis happens.
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## Comparison
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| Dimension | LLM Wiki | Semantic RAG |
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|-----------|----------|-------------|
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| **How knowledge is stored** | Pre-compiled markdown pages with cross-references already built | Raw chunks in a vector database |
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| **Finding answers** | Read index → follow links → synthesize | Embed query → similarity search → assemble |
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| **Query cost** | Low — synthesis already done | Higher — re-derives on every query |
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| **Infrastructure** | Just markdown files | Embedding model + vector DB + chunking pipeline |
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| **Maintenance** | Run a lint pass | Re-embed when content changes |
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| **Scale limit** | ~hundreds of pages (index file navigation) | Millions of documents |
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| **Setup time** | 5 minutes | Hours to days |
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| **Contradiction detection** | Built in — LLM flags on ingest | Manual |
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## Verdict
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**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.
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**Over 100K pages → RAG.** The index file becomes too large to read, and embedding-based retrieval becomes more efficient than full-index scanning.
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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.
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(Source: [[LLM Wiki Pattern]], [[Compounding Knowledge]])
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