A stale fact in an AI knowledge base is a claim that was once supported but no longer represents the governing source or current operating state. The retrieval index may be technically fresh while the claim is wrong because a policy changed, a role moved, a price expired, a contract ended, or the source lost authority.
Detection must happen at claim level. Re-indexing every document on a schedule does not explain which answers changed, whether a newer source overrides an older one, or which users are allowed to see the replacement.
Give every claim a freshness contract
Store the claim with source identity, source type, location, observed date, effective date when known, entity, sensitivity, owner, derivation, and content hash. Record whether the source is canonical, supporting, observational, or historical. A copied slide and the signed policy it summarizes should not carry equal authority.
Assign a freshness rule based on the claim, not the file format. A registered address may remain valid until an official change. A price, staff role, integration status, legal requirement, and campaign schedule may need different review windows or event triggers. Some facts should expire automatically; others remain usable but carry an age warning.
The company memory architecture beyond vector search explains why source of truth, retrieval index, memory, permissions, and observed outcomes must remain separate. Freshness metadata belongs beside provenance rather than being inferred from vector similarity.
Detect change through several signals
Watch canonical sources for a new version, changed hash, modified structured field, withdrawn page, replacement document, or owner event. Compare extracted claims, not only whole files. A small amendment can reverse one important rule while leaving most text unchanged.
Use operational signals too. A failed integration check, rejected payment, changed API response, user correction, support incident, or repeated answer override may challenge a published claim. Treat the signal as evidence to investigate, not an automatic rewrite of the source of truth.
Run contradiction checks within the same entity and sensitivity boundary. Rank conflicts by source authority, effective date, and directness. Do not merge two values into a convenient average. The founder trust checklist for confident AI failures provides a broader control layer for claims that look fluent but lack verified support.
Maintain a queue with claim, trigger, affected answers, source owner, reviewer, and deadline. Prioritize high-impact claims used in sales, payments, access, compliance, or customer commitments. A low-risk descriptive fact can wait longer than a rule that authorizes an external action.
Revalidate without silently rewriting history
When evidence changes, create a new version and close the old claim with a reason and effective boundary. Preserve lineage so an audit can reconstruct what the system knew at the time. Update the retrieval representation only after the governed claim state changes.
Choose safe answer behavior for expired or conflicting knowledge. The assistant may withhold the value, name the uncertainty, retrieve the canonical source live, ask an owner, or provide historical context with a date. It should not present the last indexed value as current merely because no replacement is available.
Test freshness controls with known changes. Seed an expired price, moved role, replaced policy, revoked permission, and conflicting source. Confirm detection, routing, answer suppression, revalidation, publication, and audit trail. The verified completion and memory pattern shows why observed outcomes and receipts matter after an update claims success.
AI4SALE’s published company-memory work covers source attribution, bounded retrieval, and controlled knowledge updates, which is the evidence base for this freshness workflow. It does not demonstrate deployment volume, retrieval accuracy, certifications, or commercial results.
Frequently Asked Questions
A fact is stale when it no longer represents the governing source or current operating state, even if the indexed document itself has not expired.
No. It misses source authority, effective dates, claim-level amendments, operational contradictions, copied content, and facts that expire without a file change.
It should apply the documented authority and effective-date rules, disclose uncertainty, retrieve or request the canonical source, and avoid inventing a merged value.
Usually they should be versioned and closed with an effective boundary so the current answer changes while historical lineage remains auditable.
If you need claim-level freshness, provenance, conflict handling, and permission-aware retrieval for approved company sources, review AI4SALE enterprise AI search services. A useful implementation ends with detection tests, source-owner routes, safe answer modes, and an auditable change trail.