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Few-shot examples demonstrate the decision boundary better than a generic format request. This is not a substitute for “Correct the label before relying on the example”; that control is necessary in the stated circumstances. Inconsistent demonstrations teach conflicting rules and harm predictable output.

Balanced examples reduce implicit pressure to select only polarized labels. Here, an example gives the correct JSON shape but uses a contradictory label. Correct the label before relying on the example. Inconsistent demonstrations teach conflicting rules and harm predictable output.

Representative demonstrations clarify how to apply multiple conditions. The direct remedy is “Correct the label before relying on the example”, which the alternative does not itself accomplish. Inconsistent demonstrations teach conflicting rules and harm predictable output.

Examples should illustrate generalizable features rather than accidental surface patterns. This alternative does not establish how to teach semantic field selection beyond formatting in the stated workflow.

Examples are powerful instructions. It would leave the stated need to teach semantic field selection beyond formatting unresolved. Matching example field names to schema keys cannot establish which of several parties is legally the counterparty. Teach and evaluate the semantic selection from the contract evidence.

The model needs to see how nearby similar fields differ. Although useful elsewhere, it is not the direct mechanism to teach semantic field selection beyond formatting.

Few-shot samples shape inferred behavior. The scenario instead requires a way to measure few-shot changes on unseen representative cases. Bilingual examples are useful only if language ambiguity is actually causing errors. The question describes an evaluation claim based solely on the same prompt examples used for tuning, so held-out data is needed.

The model needs to see how nearby similar fields differ. This is not a substitute for “Evaluate on separate held-out cases that were not used as demonstrations”; that control is necessary in the stated circumstances. A stable test set also reveals whether adding more demonstrations actually improves the target behavior or merely moves errors elsewhere.

Schema-consistent examples are necessary but do not prove that prompt changes improve behavior on unseen data. A model may memorize every demonstration and still regress elsewhere. Reserve a held-out evaluation set to measure generalization separately from the prompt's examples.

Page positions are useful evidence, but asking for them in unvalidated prose does not guarantee a parseable result or ensure that every finding has its required reference. The consumer needs a schema-constrained object with explicit required fields and a post-generation check that evidence identifiers genuinely support each finding.

A prompt that merely describes a typed findings array does not constrain the API response: Claude might still return arbitrary prose, malformed JSON, or omit evidence fields. Use `output_config.format` with a supported JSON Schema, then validate the returned content and its factual source support before accepting the compliance result.

A textual requirement has no deterministic effect if the consumer persists a record that omits the evidence field. The API should enforce a typed JSON Schema and reject or explicitly route records whose mandatory evidence references are missing. Even a populated citation field must be checked for source relevance before the compliance finding is accepted.

Examples do not enforce machine-readable object structure or mandatory evidence fields. Free-form examples do not enforce which fields downstream code needs. Use a supported schema or tool contract with required properties and validate the generated object before persisting extracted findings.

Shape validation alone does not establish that required evidence is present and credible. JSON can be syntactically valid while omitting the citation or evidence for an asserted finding. Add schema and semantic checks for source references, and reject unsupported claims instead of accepting any parseable object.

Copying a different record's ID is factually wrong and unsafe. A missing customer identifier is a gap in the current document, not permission to borrow an earlier value. Carrying forward another person's ID could contaminate the record or trigger an unauthorized account action.

Forcing a nonempty tax ID when the document lacks one encourages fabrication, and guessing can attach the wrong identity to a record. Permit null for genuinely absent evidence and explicitly prohibit inference; the structured result should preserve uncertainty rather than manufacture an identifier.

An output tool with a schema is valuable, but a schema that defines findings as an arbitrary object still permits incompatible shapes. Define findings as an array of typed items with required fields, then validate each item so the issue list is consistently machine-readable.

The alternative, “Constrain severity to an enumerated set and verify the selected value”, can matter in another part of the workflow. Here, a tool schema accepts an arbitrary object for findings, causing inconsistent lists of issues. Define findings as an array of typed objects with required fields. Explicit nested item structure lets a downstream consumer check every extracted line item for required fields, types and allowable values.

Machine-readable financial data needs unambiguous types and units. That addresses a different part of the system, not the requirement to type each record inside structured output arrays.

Currency-specific numeric validation is appropriate for financial amounts, not qualitative review severity. Values such as severeish must map to an approved severity enum; checking currency or numeric types does not prevent unsupported classification labels.

Strict schemas prevent unreviewed expansion and accidental drift. This alternative does not establish how to standardize severity fields in schema-bound outputs in the stated workflow.

Explicit item structure makes downstream consumers predictable. The decisive requirement here is to standardize severity fields in schema-bound outputs. An array-of-objects schema does not restrict the severity field to known values. The defect is that arbitrary severity strings cannot reliably map to permitted workflow categories.

Syntactically valid JSON can contain finding objects that omit the evidence snippet. Parsing without checking each nested record leaves that omission undetected. Define evidence as a required field for every finding and reject records whose evidence field is missing or unusable; then verify the cited source actually supports the finding.

An array type alone does not guarantee that each finding contains supporting evidence. Making the evidence property optional, or accepting an empty string, preserves the defect even if every other field is well typed. Require a nonempty evidence reference on every finding and reject records that violate the contract.

A prose request cannot enforce completeness when the consumer accepts an object that lacks evidence. An explicit JSON Schema can work only if the evidence field is required for each nested finding and its presence is checked. Treating citations as optional while accepting the result recreates the reported problem.

The output structure should match the data relationships. That addresses a different part of the system, not the requirement to separate schema conformance from factual accuracy.

Schema changes are interface changes. This capability does not directly separate schema conformance from factual accuracy. Versioning helps consumers adapt to changed fields, not detect that a syntactically valid critical classification is semantically unsupported. Compare the label with authoritative classification criteria.

Separating refusals and successful extraction objects is an interface safeguard, but this response already passes its schema and makes a wrong risk judgment. Evaluate the business classification criteria against source facts; valid types do not imply a harmless event is critical.

Source page references improve auditability for successful extractions, but do not tell a client that an unsupported sensitive document was refused. Use a tagged status/result variant so the consumer validates extraction fields only when the operation actually succeeded.

Syntactic validity is not factual or semantic validity. Although useful elsewhere, it is not the direct mechanism to handle structured refusals separately from successful extraction.

The output structure should match the data relationships. This alternative does not establish how to handle structured refusals separately from successful extraction in the stated workflow.

Flattening nested transactions into one opaque string removes the relationships that consumers must validate. A nested item schema preserves per-invoice identity, line items and calculations.

Syntactic validity is not factual or semantic validity. That addresses a different part of the system, not the requirement to preserve nested record relationships in structured output.

Failure-status variants handle refusals or exceptions; they do not represent two invoices with separate line items and taxes in one document. Define nested invoice objects and arrays in JSON Schema so amounts and records retain their parent-child relationships.