What is Structured Output?

Definition

Structured output refers to a language model's ability to generate responses that conform to a specific format and schema, producing machine-parsable data rather than free-form text. The most common format is JSON, where the model outputs a valid JSON object with specific fields, types, and values as defined by a schema. This capability is essential for integrating LLMs into software systems where downstream code needs to parse and process the model's output programmatically.

Structured output can be achieved through several approaches. Prompt-based approaches instruct the model to output in a specific format and hope for compliance — simple but unreliable. Constrained decoding approaches modify the generation process to enforce structural validity — at each token position, the model can only choose from tokens that maintain valid structure. Schema-based approaches (like OpenAI's Structured Outputs) combine training with constrained decoding to guarantee that output conforms to a provided JSON schema.

The reliability of structured output is critical for production applications. A model that generates valid JSON 95% of the time will cause errors in 1 out of 20 requests — unacceptable for production systems. Constrained decoding eliminates this failure mode entirely by making it impossible for the model to generate structurally invalid output. However, structural validity does not guarantee semantic correctness — the model can still put wrong values in correctly formatted fields.

Why It Matters

Most production LLM applications require structured output. Entity extraction, classification, data transformation, API integration, and tool calling all need the model to produce output in a specific format that code can parse reliably. Without structured output, every LLM integration requires fragile regex-based parsing of free-text responses, which breaks whenever the model slightly changes its response format.

Structured output also enables type safety in LLM applications. By defining output schemas using JSON Schema or Pydantic models, developers can validate model outputs at the type level, catching errors at the boundary between the AI system and the application logic. This makes LLM-powered systems more robust and easier to debug.

How It Works

Constrained decoding — the most reliable approach — works by modifying the token sampling process during generation. A finite-state machine or parsing automaton tracks the current structural state (e.g., 'inside a JSON object, expecting a key name'). At each generation step, the automaton determines which tokens are structurally valid in the current state and masks out all other tokens before sampling. This guarantees that every generated token maintains structural validity.

For JSON schema compliance, the system compiles the JSON schema into a set of structural constraints. Required fields must appear, field values must match their specified types (string, number, boolean, enum), and additional fields may be prohibited. The constrained decoder enforces these constraints throughout generation, making it impossible for the model to produce output that violates the schema. Libraries like Outlines and Instructor implement this approach for open-source models.

Example Use Case

A document processing pipeline uses an LLM to extract invoice data into a structured format: {vendor: string, amount: number, date: string, line_items: [{description: string, quantity: number, price: number}]}. Using constrained decoding with this JSON schema, the model produces perfectly formatted JSON for every invoice, which flows directly into the accounting system without any parsing failures. Before structured output, 8% of extractions failed due to malformed JSON, requiring manual processing.

Key Takeaways

• Structured output enables models to generate machine-parsable data conforming to a defined schema.
• Constrained decoding guarantees structural validity by masking invalid tokens during generation.
• Structural validity does not guarantee semantic correctness — values can still be wrong.
• Reliable structured output is essential for integrating LLMs into software systems.
• JSON Schema and Pydantic are common ways to define expected output structure.