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A typed output contract helps reject invalid fields and missing required properties. Here, a nested list must contain one or more properly typed evidence entries. Constrain item structure and required evidence fields with JSON Schema. Nested validation prevents malformed provenance elements from reaching downstream systems.

Allowing null for a legitimately unknown date prevents fabrication, but does not constrain the structure of nested evidence items. This list requires array item schemas, required provenance fields and a minimum length so a consumer can validate each cited item.

Finite state values prevent consumer failures caused by arbitrary label variants. The proposed option cannot provide the specific “Constrain item structure and required evidence fields with JSON Schema” behavior that the case requires. Nested validation prevents malformed provenance elements from reaching downstream systems.

The output structure should match the data relationships. This alternative does not establish how to verify actual structured object shape after parsing in the stated workflow.

Requiring an evidence snippet is important for audit, but the parser is receiving JSON text nested inside another JSON string. Confirm that the decoded result is the expected object, not a string containing serialized JSON, before field-level evidence validation.

Restricting severity values does not solve an output that is a JSON string rather than the required JSON object. Validate the decoded top-level data type against the schema and handle double serialization as an explicit format error.

A refusal/success variant is needed when extraction may legitimately be declined, but these successful numbers need precise provenance. Include page numbers or source-span identifiers alongside each extracted numeric value so reviewers can verify its origin.

Nested invoice line items preserve structural relationships, but do not by themselves identify the source page or quoted span for a number. Add provenance fields to extracted values or their parent records so an auditor can trace every amount to its documentary evidence.

Tool schemas constrain the shape of generated arguments. Here, a document extraction result needs to support audit of each quoted number. Include source references or page positions alongside extracted values in the schema. Typed provenance lets reviewers trace a value to evidence.

A blind retry can repeat the same missing field indefinitely. Repeating the same invalid request with no feedback is unlikely to repair the missing field. Bound retries and report precisely which schema property failed so the next attempt can correct it.

Format validation is not a factual accuracy test. A parseable JSON object can still omit required fields or mislabel business values. Schema conformance and source-based validation must be checked separately before an extraction is accepted.

Precise diagnostics help the model target the defect. Although useful elsewhere, it is not the direct mechanism to avoid hallucinated corrections when the source has no fact.

Repeatedly guessing dates not present on the receipt cannot create source evidence. The correct outcome is an explicit missing-value state, with human verification when the purchase date is needed for the business operation.

Deterministic validators can reject invented dates, but repeated attempts on the same receipt cannot recover information absent from the image. Return a missing-date status and request authorized review if needed; repeatedly generating new guesses is not evidence-based correction.

Structural validity cannot identify semantic misassignment. The scenario instead requires a way to limit repeated extraction corrections. Semantic field checks are necessary for correctness, but they do not bound an extractor's repeated repair attempts after three schema failures. Enforce a retry budget and terminal error route.

Self-reported confidence is not a machine-verifiable guarantee that an object matches the required schema. Here, a JSON extractor fails schema validation three times. Set a maximum retry count with an explicit fallback or human-review outcome. Retries consume cost and may never resolve incompatible source data.

Business invariants such as mutually exclusive statuses validate content, but cannot by themselves cap an extraction retry loop. After repeated schema failures, use a maximum attempt count and explicit human-review or failure outcome so costs and side effects remain bounded.

Structural validity cannot identify semantic misassignment. The decisive requirement here is to provide actionable validation detail for retries. Comparing values with source labels cannot help a model repair an unspecified validator error. Return field-level validation details and allowed formats first.

Validating formats regardless of confidence determines acceptance, but a vague 'invalid' response leaves the model without a repair target. Return the field path, expected type and observed invalid value so any bounded retry can correct the specific failing field.

Transient service failures may recover. This capability does not directly provide actionable validation detail for retries. Exponential backoff is useful for transient transport or rate limits, not a deterministic malformed field in the current output. The payload must be corrected using precise diagnostics.

Generic format and business validation is appropriate, but the precise defect is arithmetic inconsistency between invoice line items and a grand total. Recalculate totals deterministically, including tax and rounding rules, and return the actual numerical mismatch for correction.

A corrective retry needs to know which field failed and why. This alternative does not establish how to verify numeric consistency independently of generated prose in the stated workflow.

Retries consume cost and may never resolve incompatible source data. Although useful elsewhere, it is not the direct mechanism to verify numeric consistency independently of generated prose.

A schema may allow both booleans independently. That addresses a different part of the system, not the requirement to catch meaning errors that schema validation misses.

Retries consume cost and may never resolve incompatible source data. The direct remedy is “Compare field values to the source's semantic labels or rules, not just JSON types”, which the alternative does not itself accomplish. Compare addresses against the source labels, extraction location and business definitions; reject a swapped billing-versus-shipping interpretation.

A field-specific schema error would help if a value had the wrong type. Here both shipping and billing addresses may be valid strings while placed in the wrong semantic fields. Compare them with source labels and business relationships, not only JSON validation feedback.

Transient service failures may recover. The decisive requirement here is to measure the causes and outcomes of extraction retries. Retrying an unchanged invalid identifier with a backoff will not make it valid. A bounded repair or explicit human escalation is needed for persistent validation failure.

Rejecting incompatible shipment statuses prevents contradictory records, but this extraction service is stuck retrying without improvement. Record the attempt number, validation failures, input revision and final disposition so operators can diagnose repeated failures and enforce a bounded retry policy.

Replacing an entire record can introduce fresh errors in already-correct fields. This alternative does not establish how to measure the causes and outcomes of extraction retries in the stated workflow.

The alternative, “Retry with bounded exponential backoff and an idempotent operation identifier”, can matter in another part of the workflow. This is not a substitute for “Ask for a bounded correction of the invalid field while preserving validated fields; Enforce record-level invariants after the field correction; Revalidate the corrected postal code and its schema constraints before persisting”; that control is necessary in the stated circumstances. When nineteen fields have passed validation, request a correction to only the invalid postal code and preserve the other field values and their evidence.

Deleting validation history removes evidence of why the field was corrected and whether the replacement satisfied the schema. Keep the prior validated fields intact, revalidate the corrected field and record the result before persisting.

Discarding confirmed data creates needless regression opportunities. The nineteen verified fields should remain stable while the defective postal code is repaired. Replacing them with guesses needlessly expands the error surface and makes the new output less traceable.