Turn scattered business information into a governed knowledge service for AI and human support.
An AI knowledge base is not a folder of everything the organization can find. It is a controlled set of authoritative sources with owners, audiences, effective dates, access rules, answer boundaries, and tests showing what should be retrieved—or refused.
Define authority before ingestion
For each subject, identify the policy owner and source of truth. Separate public guidance, authenticated account information, internal procedures, draft material, legal terms, product configuration, and historical records. A model should not resolve conflicts by choosing the document with the most confident wording.
Make each knowledge item operable
- Stable title, subject, audience, owner, status, and source location
- Effective date, review date, superseded version, and jurisdiction or product scope
- Clear answer, exceptions, prerequisites, and escalation trigger
- Access classification and permitted AI channel
- Structured headings, definitions, examples, and linked dependencies
- Test questions, expected source, expected answer, and refusal cases
Separate knowledge layers
| Layer | Purpose | Control |
|---|---|---|
| Public facts | Answer general questions | Current published source |
| Operational procedure | Guide authorized work | Role and version control |
| Customer-specific data | Support account action | Authentication, minimum fields, audit |
Build and test the collection
- Inventory top questions and current answers.
- Resolve conflicts with accountable owners.
- Rewrite sources for clarity without changing policy.
- Add metadata and access rules.
- Test retrieval, citation, ambiguity, and no-answer cases.
- Monitor misses and retire superseded content.
Avoid knowledge-base contamination
- Drafts and expired policies indexed as current
- Untrusted user content retrieved as authority
- One document exposed beyond its intended audience
- Chunking that separates a rule from its exception
Measure knowledge health separately from model style
Track authoritative coverage, owner coverage, overdue reviews, retrieval precision, unsupported-answer rate, conflict rate, no-answer quality, citation usefulness, and escalation. Sample real misses to decide whether the gap belongs in knowledge, workflow, training, or deliberate refusal.
Continue with the next decision
Sources and further reading
Primary and contextual sources used to verify definitions or give readers a relevant next resource.
- NIST AI Risk Management Framework Primary framework supporting governance, mapping, measurement, and management of AI risks across the lifecycle.