Definition

An LLM knowledge base is a workflow where immutable source material is incrementally compiled by an LLM into a persistent, interlinked markdown wiki.

Why It Is Different From Plain RAG

  • Plain RAG retrieves raw fragments at question time.
  • This method maintains a durable synthesis ahead of time.
  • Cross-links, summaries, and contradictions accumulate instead of being rediscovered for every query.

Working Model In This Repo

  • Source material lives outside wiki/ and is treated as immutable.
  • wiki/sources/ holds source summaries.
  • wiki/concepts/ holds cross-source synthesis.
  • wiki/queries/ and wiki/reports/ hold durable outputs from asking questions.
  • wiki/index.md and wiki/log.md help navigation and maintenance.

Contrast With AI Knowledge Bases

  • Many AI knowledge base products focus on question-time grounding over uploaded documents.
  • An LLM-maintained wiki adds a persistent synthesis layer: summaries, cross-links, contradiction tracking, and updated concept pages.
  • The two approaches can complement each other: an AI knowledge base can help with retrieval, while the wiki preserves durable synthesis.

Current Evidence In Repo

Practical Loop

  1. Add a source to one of the immutable source folders outside wiki/.
  2. Ingest it into wiki/sources/.
  3. Update any affected concept pages.
  4. Ask questions against the wiki.
  5. Save useful answers back into the wiki.
  6. Periodically lint for missing links, stale claims, and gaps.

Early Takeaway

Even at small scale, this method can be useful because the wiki index and concise summaries reduce repeated search effort and make later questions easier to answer consistently.