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A model can generate plausible claims beyond its source material, especially when questions are ambiguous or evidence is sparse.
Create tests with unanswerable and conflicting inputs, require source grounding for factual claims where feasible, and review a risk-based sample of outputs. Track unsupported claims separately from formatting errors and provide a correction or escalation path.
MLOps and AI governance can define output quality checks and escalation. Informatix Systems can assess the symptoms, evidence, and next steps. Contact Informatix Systems to discuss this issue.
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