Structured Output

Structured output refers to techniques for constraining LLM responses to specific formats, typically JSON schemas, ensuring the output can be reliably parsed and processed by downstream systems.

Why It Matters

Free-form text outputs are unpredictable. They might include markdown formatting, natural language explanations, or slight variations in structure that break parsing. Structured output solves this by guaranteeing:

• Reliable parsing: Output always matches expected schema
• Type safety: Fields have correct types (strings, numbers, booleans)
• Required fields: Critical data is never missing
• Integration simplicity: Direct use in APIs and databases

For AI engineers building production systems, structured output transforms LLMs from text generators into reliable data extraction and transformation tools.

Implementation Basics

Common approaches:

1. Native API support: OpenAI’s Structured Outputs, Anthropic’s tool use
2. Grammar-based: Constrain token generation to valid JSON paths (Outlines, Guidance)
3. Validation libraries: Instructor, Pydantic for schema enforcement
4. Post-processing: Parse and validate with fallback prompting

OpenAI example with Pydantic:

from pydantic import BaseModel

class ExtractedData(BaseModel):
    name: str
    email: str
    sentiment: str

response = client.beta.chat.completions.parse(
    model="gpt-4o",
    messages=[...],
    response_format=ExtractedData
)

Best practices:

• Define schemas with clear field descriptions
• Use enums for categorical fields
• Include examples in prompts for complex structures
• Implement retry logic for validation failures

Structured output is essential for any application where LLM responses feed into automated workflows, databases, or APIs.