Structured Outputs

Overview

Structured outputs are a method of constraining AI model responses to follow specific, predefined formats rather than generating free-form text. This approach ensures that AI outputs conform to exact schemas or patterns, making them directly usable by downstream applications and systems. When an AI model uses structured outputs, it generates data in a consistent, machine-readable format, typically following specifications like JSON schemas or XML definitions.

Core Concepts

Schema Definition

A schema in structured outputs defines the exact format the AI must follow, including:

• Required and optional fields
• Data types for each field
• Nested structure relationships
• Allowed values or ranges

For example, a weather information schema might specify:

{
  "location": "string",
  "temperature": "number",
  "conditions": ["sunny", "cloudy", "rainy"],
  "humidity": "percentage"
}

Output Enforcement

Structured outputs employ several mechanisms to ensure compliance:

• Schema validation during generation
• Type checking of output values
• Format verification before delivery
• Error handling for non-conforming outputs

Implementation

Common Formats

AI systems typically use these structured formats:

• JSON for web APIs and data exchange
• XML for document-based systems
• Key-value pairs for simple data structures
• Tabular data formats

Technical Requirements

Implementing structured outputs requires:

• Clear schema definitions
• Output validation systems
• Error handling procedures
• Integration specifications

Applications

Structured outputs are particularly valuable in:

Data Extraction

• Pulling specific fields from documents
• Converting unstructured text to databases
• Extracting entities and relationships (Named Entity Recognition)
• Standardizing information formats

System Integration

• API response formatting
• Database record generation
• Workflow automation
• Cross-system data exchange

Related Concepts

• Function Calling
• System Instructions
• Task Decomposition