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Observable class boundaries prevent an underspecified instruction from yielding systematic overclassification. This is not a substitute for “Allow a missing-date status and prohibit invented time information”; that control is necessary in the stated circumstances. A realistic output contract must handle absent inputs without incentivizing hallucination.

A schema rule forcing a date where none was provided induces fabrication. The prompting contract should explicitly permit a missing-date state and forbid guessed schedule dates, with clarification or documented exceptions where an actual date is required.

Output criteria provide a stable target for evaluating summaries. The direct remedy is “Allow a missing-date status and prohibit invented time information”, which the alternative does not itself accomplish. A realistic output contract must handle absent inputs without incentivizing hallucination.

Requiring citations and calibrated confidence can expose unsupported classifications, but the classifier's recurring error is treating harmless announcements as incidents. Add explicit non-incident examples and exclusion criteria to the rubric so factual notices are not automatically interpreted as threats.

Operational categories need boundaries. The scenario instead requires a way to reduce false positives with explicit exclusion conditions. Bug-versus-feature precedence rules are unrelated to a service-announcement classifier. That task needs inclusion and exclusion cases that distinguish routine announcements from true incidents.

Overbroad safety language can suppress legitimate work. This alternative does not establish how to reduce false positives with explicit exclusion conditions in the stated workflow.

Redundant examples waste context without broadening case coverage. Numerous redundant examples consume context without teaching meaningful distinctions. Select a small representative set of borderline and contrasting cases so the model learns decision boundaries while retaining room for the actual document.

The evaluation still needs a defined output contract. Formatting and required-field instructions remain part of the consumer contract. Removing them invites inconsistent outputs even when a few examples happen to show the desired layout; preserve explicit requirements and validation.

Example selection must respect data minimization and privacy. This alternative does not establish how to cover minority input patterns with appropriate examples in the stated workflow.

Near-boundary complaint examples help sentiment classification, but do not teach an invoice extractor how bilingual field labels map to required values. Add representative invoices in both languages with annotated label-to-field interpretations and validate on unseen bilingual documents.

Held-out evaluation is essential for measuring generalization, but it does not supply the missing bilingual training demonstrations by itself. Include representative bilingual examples with correct field annotations, then use a separate held-out set to test whether they generalize.

Bilingual invoice examples address multilingual extraction, whereas this complaint classifier struggles with polite requests near a decision boundary. Supply contrasting polite complaint and noncomplaint cases, with explicit reasons for each label, rather than unrelated language coverage.

Bad examples teach bad behavior. The scenario instead requires a way to teach decision boundaries through contrasting cases. Removing a false example is sensible when one is known to be wrong, but the case asks how to teach a borderline distinction. Use paired near-boundary examples with verified opposite labels.

Good performance on examples inside a prompt may be memorization, not reliable classification of real user inputs. The required action is “Pair near-boundary complaint and noncomplaint examples with explicit labels”; the other measure addresses a separate concern. The few-shot set should show near-boundary polite requests that are genuine complaints alongside similarly phrased messages that are not complaints, with the expected label and the deciding signal for each.

Example selection must respect data minimization and privacy. That addresses a different part of the system, not the requirement to align few-shot exemplars with the live output contract.

Multi-party document examples teach which person or entity a field belongs to, but cannot fix a production output schema whose key changed from amount_usd to total_amount. Update the examples to use the exact current field names and verify that consumers parse them.

Contrasting invoice, due and service dates helps date-field disambiguation. This example instead uses the wrong output key for a monetary amount. Align all demonstrations with total_amount in the deployed schema; unrelated date examples would leave the integration broken.

Schema shape alone does not teach semantic judgment. This alternative does not establish how to avoid spurious few-shot dependence on one layout in the stated workflow.

Removing a bad example is necessary if its label is wrong, but the supplied examples are all from one vendor and may be individually correct. Improve representativeness with different valid layouts and counterparties, while maintaining the same extraction rule and testing on held-out contracts.

Repeating examples from the same vendor reinforces the narrow layout pattern that already caused false rejections. Omitting cross-vendor tests also hides whether the classifier generalizes. Use representative samples spanning other legitimate structures, and measure changes on held-out layouts instead of saturating the prompt with duplicates.

Examples teach the model concrete field names and output conventions, so a conflicting key in an example can undermine a correct prose instruction. The direct remedy is “Show distinct invoice, due and service dates in annotated contrasting samples”, which the alternative does not itself accomplish. Annotated counterexamples showing invoice date, due date and service date force the extractor to associate each value with the correct label.

A collection containing only one date per invoice does not expose the ambiguity between invoice date, payment due date and service period. The extractor can continue mapping every date-shaped field to invoice_date because it has never seen contrasting, labelled roles. Include correctly annotated multi-date examples so the role depends on context, not a date regex.

De-identification is essential when examples contain real customer documents, but privacy protection alone does not teach the difference between invoice_date and due_date. Include correctly labelled contrasting date fields with annotated source locations so the extractor learns the intended semantics.

Adding a new-form regression example solves a coverage gap for an updated input format, not an assistant whose long diffuse tone policy is ignored. Compress the guidance into a few crisp rules and approved example responses, then test for consistent style.

Examples are powerful instructions. This capability does not directly teach desired response tone with examples. Schema-example synchronization prevents malformed outputs, but the reported problem is inconsistent customer tone. Concise style criteria and representative approved responses address the actual failure.

Broader document-layout coverage supports extraction generalization, but this problem concerns tone consistency under a twelve-paragraph instruction. Shorten the style rubric and use a small set of approved response examples; more document layouts would only add irrelevant context.

Diversity across valid examples improves generalization, but cannot compensate for an incorrect answer key in a current demonstration. Fix or remove the mislabeled sample first, since the model is learning contradictory supervision.

Aligning output field names with the schema prevents parsing errors, but does not repair a mislabeled answer inside a few-shot example. The teaching signal itself is wrong. Correct that example's key or exclude it before introducing more samples.

Boundary-case examples are useful for ambiguous complaint labels, but adding them before repairing an incorrect example preserves contradictory supervision. Audit and correct the flawed demonstration first; then add balanced boundary examples only if evaluation still shows a coverage gap.

Few-shot examples should represent the document structures that the classifier will actually encounter. This is not a substitute for “Balance example ordering and test for order sensitivity on a held-out set”; that control is necessary in the stated circumstances. Measure performance against independently labelled cases rather than assuming the model learned the stated rubric.

Contrasting examples demonstrate which subtle features change the classification. This capability does not directly detect example-order artifacts in classification. Contrasting complaints and neutral inquiries can help, but the question specifically describes order sensitivity caused by the last example. Balance and reorder samples, then evaluate independently.

Example selection must respect data minimization and privacy. That addresses a different part of the system, not the requirement to detect example-order artifacts in classification.