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How To Build an LLM Wiki

Build an LLM Wiki in layers: define scope, create the starter structure, add metadata and trust labels, seed the first authoritative pages, establish review workflows, then connect retrieval only after the source is curated.

Step-By-Step Build

  1. Choose a home. Use a private repository, docs-as-code folder, wiki platform with exportable files, or internal documentation system that preserves history and permissions.
  2. Create the starter structure. Start with README, INDEX, GOVERNANCE, TRUST_MODEL, CONTRIBUTING, CHANGELOG, organization, architecture, operations, decisions, policies, agent, and onboarding folders.
  3. Define metadata. Require title, owner, status, trust level, last reviewed date, review cycle, audience, sensitivity, agent use, and human-review rules.
  4. Assign owners. Every authoritative page needs an accountable owner before agents or humans rely on it.
  5. Create the trust model. Define authoritative, working-draft, historical, deprecated, proposal, external-reference, and needs-review labels.
  6. Add the first core pages. System overview, glossary, decision log, open questions, runbooks, AI usage policy, agent instructions, retrieval guide, update rules, citation rules, and safety boundaries.
  7. Add contribution rules. State how humans and agents propose changes, how review works, and what cannot be added.
  8. Add redaction rules. Keep secrets, raw customer data, regulated data, private keys, and sensitive strategy out unless explicit governance and access controls exist.
  9. Add linting. Check broken links, stale review dates, missing owners, missing metadata, duplicate pages, unresolved contradictions, and uncited claims.
  10. Add retrieval systems last. RAG, search, graph APIs, and agent tools should consume the curated wiki, not replace governance.

First Week Plan

Day Outcome Done means
1 Scope and skeleton Starter bundle installed; README and INDEX drafted.
2 Governance and trust Owners, trust labels, sensitivity labels, and approval rules named.
3 Core knowledge System overview, glossary, decision log, and open questions seeded.
4 Operations Runbooks, release/support process, incident-update rule, and escalation path added.
5 Agent readiness Agent instructions, retrieval guide, update rules, citations, and safety boundaries reviewed.

Implementation Warning

Do not wait for perfect tooling. A small, reviewed folder with clear metadata beats a large unowned knowledge platform. The mature system can later add search, embeddings, graph storage, exports, and AI Memory bundles.