Paradigm Comparison: RAG vs LLM Wiki

Two fundamentally different approaches to building AI knowledge infrastructure.

Pipeline Overview

RAG Pipeline
────────────────────────────────────────────────────────
Raw docs → chunk → embed → vector store
                                         ↘ [query time]
                              query → ANN search → LLM → answer


LLM Wiki (Knowledge Compilation)
────────────────────────────────────────────────────────
Raw docs → LLM compiles → structured wiki pages → FTS index
                                                           ↘ [query time]
                                    query → FTS / graph → read page → answer

Dimension-by-Dimension Comparison

Dimension	RAG	LLM Wiki
Core operation	Retrieve at query time	Compile at write time
Storage	Vector DB + raw chunks	Structured Markdown + SQLite FTS
Search	ANN similarity search	FTS5 BM25 + graph traversal
Knowledge form	Implicit (vectors)	Explicit (readable Markdown)
Auditability	Low	High (git diff, lint)
Multi-hop reasoning	LLM-dependent	Via related graph links
Embedding required	Yes	No (pure FTS)
Knowledge accumulation	None (static index)	Compounds over time
Token cost at query time	High (raw chunks passed to LLM)	Low (pre-compiled page)
Latency	Higher (ANN + LLM)	Lower (FTS sub-millisecond)
Infrastructure	Vector DB required	SQLite only
Human readability	No (vectors opaque)	Yes (plain Markdown)
Best for	Broad doc Q&A, real-time/live data	Long-term knowledge, agent memory, research

When to Use Each

Choose RAG when:

• You need to query a large, dynamic corpus that changes frequently
• Real-time or near-real-time data ingestion is required
• Semantic similarity across heterogeneous documents matters most
• You don't need to maintain or review the knowledge layer

Choose LLM Wiki when:

• Knowledge needs to accumulate and improve over time
• Auditability and human review of knowledge are important
• Agents need to search and reason across a curated knowledge base
• You want to minimize query-time token costs
• You want offline-capable, zero-infrastructure search

They Are Not Mutually Exclusive

Many production systems combine both:

• RAG for broad retrieval across large, changing corpora
• LLM Wiki as a curated, high-quality knowledge layer for known domains

Example hybrid: raw ingestion via RAG pipeline → high-confidence results compiled into wiki pages for long-term reuse.

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See also: RAG Technologies · LLM Wiki Technologies