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Wiki Indexing Patterns

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wiki-indexingcontent-organizationchronological-loggingnavigation-patternsllm-wiki-patternknowledge-managementinformation-architecture

Systematic approaches to organizing and navigating knowledge bases, particularly in llm-wiki-pattern implementations. Two primary patterns serve different navigation needs: content-oriented catalogs and chronological logs.

Core Patterns

Content-Oriented Index (index.md)

Purpose: Catalog all wiki content organized by category and topic.

Structure:

  • Links to all wiki pages with one-line summaries
  • Organized by category (entities, concepts, sources, syntheses)
  • Optional metadata (dates, source counts, confidence levels)
  • Updated on every ingest operation

Navigation Model: LLM reads index first to identify relevant pages for queries, then drills into specific content. Works effectively at moderate scale (~100 sources, hundreds of pages) without requiring embedding-based infrastructure.

Example Structure:

## Entities (45 pages)
- openai - AI research company developing GPT models
- anthropic - AI safety company behind Claude
- andrej-karpathy - Former Tesla AI director, advocate of LLM wiki pattern

## Concepts (89 pages)  
- [retrieval-augmented-generation](/concepts/retrieval-augmented-generation) - Combining retrieval with generation
- Vector Embeddings - Dense numerical representations of data

Chronological Log (log.md)

Purpose: Append-only record of all wiki operations and evolution.

Structure:

  • Timestamped entries for ingests, queries, lint operations
  • Consistent format enabling programmatic parsing
  • Timeline of wiki evolution and recent activity
  • Context for understanding system state

Navigation Model: Understand what's been done recently, track system evolution, provide context for LLM about recent operations.

Example Format:

## [2026-12-21 14:30] ingest | Karpathy LLM Wiki Pattern
- **Source**: raw/articles/karpathy-llm-wiki-pattern.md
- **Pages touched**: [llm-wiki-pattern](/concepts/llm-wiki-pattern), [compounding-artifacts](/concepts/compounding-artifacts), [persistent-learning](/concepts/persistent-learning)
- **Summary**: Ingested comprehensive blueprint for LLM-maintained knowledge bases

Design Principles

Complementary Functions

The two patterns serve different but complementary navigation needs:

  • Index: "What knowledge exists and where is it?"
  • Log: "How did this knowledge base evolve over time?"

Parseable Formats

Both use consistent formatting that enables programmatic access:

  • Index supports category-based filtering and summary generation
  • Log enables timeline analysis with simple Unix tools (grep "^## \[" log.md | tail -5)

LLM-Maintained

Both files are automatically maintained by LLMs during normal operations, eliminating human maintenance burden while providing essential navigation capabilities.

Scaling Considerations

Moderate Scale Effectiveness

Content-oriented indexing works well up to hundreds of pages without requiring sophisticated search infrastructure. The human-readable format provides good overview while remaining LLM-navigable.

Search Enhancement

At larger scales, can be supplemented with search tools:

  • Local search engines like qmd for hybrid BM25/vector search
  • Full-text indexing for content that exceeds index-based navigation
  • MCP servers for programmatic access to search capabilities

Hierarchical Organization

Index structure can evolve to use subcategories and nested organization as content volume grows:

### AI/ML Core (89 pages)
### RAG & Search (47 pages)  
### Agent Systems (112 pages)

Implementation Patterns

Automatic Updates

Index updated during every ingest operation as LLM processes new sources and creates/updates wiki pages. Ensures index remains current with no human intervention.

Metadata Integration

Index can include metadata from page frontmatter:

  • Creation/update dates
  • Source counts indicating how well-researched topics are
  • Confidence levels for content reliability
  • Tag summaries for topic clustering

Cross-Reference Support

Index serves as hub for cross-reference discovery - LLM uses it to find related pages when updating content or answering queries requiring synthesis across topics.

Alternatives and Extensions

Graph-Based Navigation

Tools like Obsidian's graph view provide visual representation of page connections, complementing text-based index navigation.

Dynamic Queries

Obsidian's Dataview plugin can generate dynamic indexes based on page metadata, automatically categorizing content based on tags or other frontmatter fields.

Search Integration

Can be combined with full-text search engines for complex queries while maintaining human-readable overview structure.

See also