The pipeline must preserve source evidence while making inconsistencies explicit. Option D records the amount stated on the invoice separately from the total derived from extracted line items. A mismatch then becomes a machine-detectable validation condition rather than an invisible extraction defect.

This approach is superior because it does not silently overwrite source data or ask another model to guess which value is correct. Anthropic’s evaluation guidance recommends automated, code-based grading whenever the criterion can be expressed deterministically. Arithmetic reconciliation is precisely such a criterion. ( https://docs.anthropic.com/en/docs/build-with-claude/develop-tests ) In production, the summation should preferably be calculated by application code using normalized decimal values, even though the option describes the model populating calculated_total . The essential design principle remains the same: preserve stated_total , compute an independent total, compare them, and route discrepancies for adjudication.

Option A might improve behavior but cannot resolve genuine OCR corruption and could encourage the model to modify extracted values merely to create mathematical consistency. Option B introduces a second probabilistic judgment without new evidence. Option C is unacceptable for accounting data because it fabricates adjusted amounts and destroys fidelity to the invoice.

The schema should therefore expose both values and attach a validation status or review reason when they differ.

Official references/topics: Deterministic Validation; Human-in-the-Loop Review; Structured Output Design; Source-Fidelity Controls.