Structured Outputs
A capability that constrains a large language model to return responses in a specific, validated format such as JSON, guaranteeing schema compliance and making outputs directly machine-readable.
What are Structured Outputs?
Structured outputs is a capability that forces a large language model to produce responses that conform to a predefined schema — most commonly JSON. Rather than hoping the model follows formatting instructions in a system prompt, structured outputs guarantee schema compliance at the inference level, eliminating parsing errors and reducing downstream failures.
Understanding Structured Outputs
Building reliable LLM pipelines requires predictable output formats. Without structured outputs, teams spend engineering time on fragile regex parsers, retry loops, and defensive code to handle malformed responses. Structured outputs solve this at the source.
Key implementation approaches include:
- JSON Mode: A provider-level flag that instructs the model to always return valid JSON, though not necessarily matching a specific schema.
- JSON Schema Enforcement: A stricter mode that validates output against a developer-supplied JSON Schema, guaranteeing field names, types, and required properties.
- Tool / Function Calling: Defining a function signature and having the model return arguments in a typed schema.
- Grammar-Based Constrained Decoding: At the inference layer, constraining token sampling to only emit tokens that keep the output valid against a grammar.
Benefits of Structured Outputs
- Reliability: Eliminates parse errors — the output is always schema-valid.
- Reduced Latency: Fewer retry loops mean lower end-to-end latency.
- Easier Integration: Downstream services consume typed, validated data without defensive parsing.
- Better Testing: Schema-validated outputs are easier to evaluate and regression-test.
- Agentic Workflows: Agents that call tools or route decisions require structured outputs to pass data between steps reliably.