Structured Output
Forcing an LLM to return valid JSON (or a defined schema) instead of free-form text.
What is Structured Output?
For most production work, you need the LLM to return parseable data — JSON, XML, a table — not prose. Structured output features (OpenAI Structured Outputs, Anthropic tool use, Gemini JSON mode) constrain the model's decoding so the output is guaranteed to be valid against a schema.
Pre-structured-output, engineers wrote prompts like "respond only in JSON" and prayed. The model would still sometimes return ```json prefixes, extra fields, or malformed quotes. Modern structured output makes parse failures effectively impossible.
Schemas typically use JSON Schema or Pydantic models. The vendor compiles the schema into a constrained decoding mask that only allows valid next tokens. Latency cost is low; reliability cost is huge.
Structured output is the single biggest reliability upgrade for production Gen AI. Every engineer building data extraction, classification, or agent tool calls needs it.
A Bangalore e-commerce team was losing 4% of requests to JSON parse failures from GPT-4o. Switching to Structured Outputs with a Pydantic schema dropped parse failures to 0% — and let them remove 200 lines of defensive parsing code.
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