Structured Generation

Structured generation refers to techniques that constrain AI model outputs to follow specific formats — JSON, XML, YAML, or custom schemas — rather than producing free-form text. This ensures that AI outputs can be reliably parsed and processed by downstream systems without error-prone text extraction.

Why structured generation matters

When AI is part of a larger system — extracting data from documents, populating databases, or feeding into business logic — free-form text is unreliable. The model might format a date as "January 5th," "5/1/2025," "2025-01-05," or "fifth of January." Structured generation eliminates this variability.

Approaches to structured generation

• Prompt-based: Include format instructions and examples in the prompt. "Respond with a JSON object containing 'name', 'date', and 'amount' fields." This works reasonably well but is not guaranteed — the model might occasionally break the format.
• JSON mode: AI providers offer modes that guarantee the output is valid JSON. The model can produce any valid JSON, but you know it will be parseable.
• Schema-constrained: Provide a JSON Schema that defines exactly which fields are required, their types, and valid values. The model is constrained to produce output matching the schema.
• Grammar-constrained: For local models, tools like llama.cpp can enforce a formal grammar on the output, guaranteeing it matches a specific pattern at the token level.
• Library-based: Tools like Instructor, Outlines, and Marvin provide Pythonic interfaces for defining output schemas and automatically constraining model outputs.

How schema-constrained generation works

1. Define a schema (often as a Pydantic model in Python or a JSON Schema).
2. The system modifies the model's token probabilities at each generation step, setting the probability of tokens that would violate the schema to zero.
3. The model can only generate tokens that lead to valid outputs.
4. The result is guaranteed to match the schema — no parsing errors, no missing fields.

Practical examples

• Data extraction: Extract structured data from invoices: {"vendor": "...", "amount": 1234.56, "date": "2025-01-15", "items": [...]}
• Classification: Force output to be one of predefined categories: {"sentiment": "positive" | "negative" | "neutral"}
• Entity extraction: Pull structured entities from text: {"people": [...], "organisations": [...], "locations": [...]}
• Decision outputs: Structured reasoning with a required format: {"decision": "approve" | "reject", "confidence": 0.85, "reasoning": "..."}

Benefits for production systems

• Reliability: Guaranteed valid output eliminates the parsing failures that plague prompt-only approaches.
• Type safety: Fields have defined types (string, number, boolean, array), enabling robust downstream processing.
• Validation: Required fields, enum constraints, and value ranges catch model errors before they propagate.
• Consistency: Every response follows the same format, simplifying monitoring, logging, and analytics.

Limitations

• Constraining output format can slightly reduce response quality — the model has fewer degrees of freedom.
• Complex nested schemas may confuse smaller models.
• Schema constraints add a small amount of latency to generation.