Data: missing, stale, failed quality
Semantics: metric, time, scope ambiguous
Access: field or range unauthorized
Capability: forecast or causality unvalidated
Abstention is a reliability mechanism
Natural language omits time, scope, and definitions. Guessing hides uncertainty behind a confident number.
State what is missing, why execution should stop, what the user can add, and whether a safe alternative exists.
Separate clarification, denial, and degradation
Clarify competing definitions, deny unauthorized access without exposing schema details, use the last valid cutoff for delayed data, and bound unsupported prediction or causal claims.
Do not collapse all three into “query failed.”
Use structured answerability checks
Before execution validate metric, dimensions, filters, time, identity, freshness, and quality. Missing critical slots trigger clarification; policy denials never route back to the model.
Show inferred defaults such as calendar month and paid orders so users can correct them.
Offer an actionable next step
Suggest a candidate metric, smaller range, access request, data refresh, or descriptive analysis. Never suggest wording tricks to bypass policy.
Track abstention class and resolution to discover semantic gaps, permission errors, and demand.
Test deliberately unanswerable cases
Include ambiguous metrics, future data, unauthorized regions, tiny sensitive groups, causal claims, and missing fields.
AskTable.ai supports query, follow-ups, and enterprise access directions; confidence, quality signals, and abstention behavior require project verification.
Public references
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