Structured Output

The core mechanism that forces a model to generate tokens that conform to a predefined grammar, typically a JSON Schema. Instead of free-text, the model's logits are masked at each step to only allow tokens valid within the specified structure.

• How it works: A formal grammar (e.g., a Pydantic model, TypeScript interface, or GBNF grammar) is compiled and applied during the sampling phase.
• Legal application: Guarantees that an extracted contract clause always contains the required parties, effective_date, and obligations fields without missing keys.
• Key distinction: This is fundamentally different from simply asking a model to 'output JSON' in a prompt. Schema-constrained generation provides a mathematical guarantee of structural validity, not just a statistical hope.

The application of structured output to parse unstructured legal prose into discrete, typed objects representing real-world legal concepts.

• Entities extracted: Parties (plaintiff, defendant), obligations (payment, delivery), dates (effective_date, termination_date), jurisdictions, and governing law.
• Format: Typically a JSON array of objects, each with a type field and a mention_text span.
• Downstream value: Populates a Legal Knowledge Graph or contract management system directly, eliminating manual data entry and enabling automated obligation tracking.

A structured output design pattern that separates the model's internal reasoning process from its final, actionable answer. This is critical for auditability in legal contexts.

• Structure: The output schema defines two distinct fields: analysis (a free-text chain-of-thought) and conclusion (a strictly typed value, e.g., a boolean for 'is_force_majeure_applicable').
• Why it matters: A downstream system can parse the conclusion to trigger an automated workflow, while a human reviewer can inspect the analysis field to audit the logic.
• Hallucination guard: The analysis field can be cross-referenced against the conclusion to detect reasoning-result mismatches.

The software engineering practice of building robust parsers around structured output to handle the rare cases where a model still fails to produce valid syntax.

• Retry logic: If a JSON parse fails, the system can automatically retry the generation with a stronger schema prompt or a different sampling temperature.
• Regex repair: Lightweight, post-hoc fixes for common errors like trailing commas or unescaped quotes before passing to a standard JSON parser.
• Fallback strategies: Defining a default, safe output (e.g., { "risk_level": "unknown" }) when all parsing attempts fail, ensuring the pipeline never crashes on malformed model output.

Extending structured output beyond text to include references and coordinates from non-textual legal evidence.

• Document bounding boxes: The model outputs a structured object containing the extracted text and its spatial coordinates on a scanned PDF page.
• Table extraction: Complex tables from financial exhibits are output as structured CSV or JSON arrays, preserving row-column relationships.
• Redaction instructions: The model generates a structured list of bounding boxes to be programmatically redacted, ensuring sensitive information is removed deterministically from a production set.

Programmatic constraints and validation layers implemented around a language model to enforce specific legal and ethical policies. In the context of structured output, guardrails validate that generated JSON conforms to expected schemas and does not contain prohibited content.

• Prevents the disclosure of privileged information in structured fields
• Validates citation formats before passing data to downstream systems
• Can be implemented as regex filters, schema validators, or secondary classifier models

A security vulnerability where a malicious user crafts an input designed to override a model's system prompt or structured output formatting instructions. An attacker might inject instructions to output JSON with manipulated field values.

• Can cause unintended legal disclosures by redirecting output schemas
• Mitigated through input sanitization and strict output validation
• Particularly dangerous when structured output feeds directly into automated legal workflows

A measure of a legal language model's accuracy in generating correct and verifiable references to legal authority within structured output fields. This metric is critical when structured JSON feeds into court filings or compliance reports.

• Evaluates whether cited case names, volumes, and page numbers actually exist
• High fidelity requires grounding in authoritative legal databases
• Often validated through automated Shepardizing or parallel citation verification systems