Structured Output
Techniques for constraining language model outputs to follow specific formats like JSON schemas, XML, or custom grammars, ensuring machine-parseable responses.
Why It Matters
LLMs naturally produce free-form text, but applications need structured data: JSON for APIs, database records, form fields, or specific formats. Structured output bridges the gap between natural language and programmatic use.
Implementation Methods
Prompt engineering: Describe the schema in the prompt. JSON mode: API-level constraint. Function calling: Define parameter schemas. Constrained decoding: Restrict token generation to valid outputs (Outlines, LMQL, Instructor).
Best Practices
Provide clear schema definitions with examples. Use Pydantic or JSON Schema for validation. Handle edge cases gracefully. Test with diverse inputs. Constrained decoding is more reliable than prompt-based approaches.