AI knowledge
AI knowledge and retrieval readiness depend on whether the system can find the right source, respect access, explain its context, and expose uncertainty.
State what users need to ask, which knowledge domains are in scope, what sources are authoritative, and what the system must decline. A bounded question makes evaluation possible.
The AI and machine-learning services route provides an internal path when retrieval readiness needs technical review.
Name the source owner, update responsibility, access rule, review condition, retention boundary, and escalation route. Retrieval should not expose information simply because it is technically discoverable.
Use clear titles, scope, dates, owners, definitions, relationships, and content boundaries. Separate instructions, exceptions, and obsolete material.
Define representative questions, expected source evidence, reviewer, acceptance condition, unsafe answer response, and unresolved ambiguity. Test missing, conflicting, restricted, and stale information.
Review unanswered questions, challenged answers, stale sources, access exceptions, and repeated ambiguity. Feed findings to the source and service owners.
A large document library is not necessarily ready. Size does not prove authority, freshness, structure, access clarity, or evaluation readiness.
Bring the knowledge source, access boundary, or evaluation question that needs a clear retrieval model.
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