An enterprise AI answer can be correct and already too old.
The query ran seconds ago. The warehouse snapshot is six hours old. A late event changes the total after the answer is delivered.
A “generated at” timestamp does not expose any of that.
For database-connected AI, define freshness as a chain:
- event time
- ingestion time
- source snapshot or replication watermark
- query time
- answer time
Then set a workflow-specific contract: source, maximum age, allowed cache age, timezone, late-arrival policy, and what happens when the threshold is exceeded.
The result should carry trusted evidence from the connector—not timestamps invented by the model:
- approved source and operation
- tenant and environment scope
- source watermark
- metric and schema version
- query time
- metadata and result-cache age
- filters, truncation, and late-arrival cutoff
- trace ID
Also separate schema, authorization, and result caches. They have different keys and invalidation rules. A result cached for one tenant must never become reusable by another because the prompt looks the same.
Fluent prose is not a freshness guarantee.
Full implementation guide: Define an answer freshness contract for a ChatGPT enterprise database connection
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