AI Grounding
AI grounding links generated claims or actions to evidence, found sources, or trusted system data. Methods and scores differ. There is no single grounding protocol.
Another example
An employee asks whether unused leave carries over. Each answer claim must point to the right section in the current handbook. It must also show the effective date and leave out old, conflicting text. A citation alone does not prove the source supports the claim.
Main parts
- 01authoritative evidence
- 02source-linked claim
- 03entailment check
Use it when
Use this term when each answer claim must link to controlled evidence or current trusted state. Then check that the evidence supports it.
Do not use it when
Do not call search, a visible citation, or a long prompt successful grounding without checking each claim.
Name used in code
claim → supporting source span → entailment check Before you ship
Check the model and prompt versions, source data, versioned evaluation set, and measures. Verify links to evidence, tool permissions, privacy, common failures, decline-to-answer and fallback behavior, human review, monitoring, cost, speed, and rollback.
Outcome: Use or evaluate AI Grounding to make AI behavior measurable and tied to evidence. User context: An employee asks whether unused leave carries over. Each answer claim must point to the right section in the current handbook. It must also show the effective date and leave out old, conflicting text. A citation alone does not prove the source supports the claim. AI method or concept: AI Grounding. Why it fits: Use this term when each answer claim must link to controlled evidence or current trusted state. Then check that the evidence supports it. Do not use it when: Do not call search, a visible citation, or a long prompt successful grounding without checking each claim. AI requirements: Define the input and source evidence. Set model and tool permissions. Use versioned evaluation data and measures. Define failure, decline-to-answer, privacy, speed, and cost limits. Operational safeguards: Keep a clear trace and hide sensitive log data. Show users a safe fallback. Mark steps that need human review. Define how to roll back the model or prompt. Acceptance criteria: Record baseline and target measures on a versioned evaluation set. Test edge cases and hostile inputs. Verify fallback, monitoring, permissions, and rollback. Evidence and limits (evidence boundary): No single official standard controls the whole term. Its meaning may change across teams, platforms, or frameworks. Unknowns to confirm: Target task, model and version, evaluation owner, source data, risk limit, tool permissions, and production fallback.
Check this request
Review the current use of AI Grounding. Definition: AI grounding links generated claims or actions to evidence, found sources, or trusted system data. Methods and scores differ. There is no single grounding protocol. Release checks: Check the model and prompt versions, source data, versioned evaluation set, and measures. Verify links to evidence, tool permissions, privacy, common failures, decline-to-answer and fallback behavior, human review, monitoring, cost, speed, and rollback. Before changing code, report the evidence you found, gaps, severity, and the smallest safe fix.