Structured Output with LLM APIs: JSON, Schemas, Validation

Many AI applications need structured output, not prose. They need JSON, fields, labels, scores, tool arguments, or database-ready records.

Structured output is powerful, but it needs validation.

Common use cases

Structured output is used for:

field extraction
classification
routing
tool calls
form filling
product tagging
lead scoring
policy checks

Use schemas

Define exactly what the model should return:

{
  "priority": "low | medium | high",
  "category": "billing | technical | account",
  "summary": "string",
  "needs_human_review": true
}

Then validate the output in code.

Expect failures

Models may return:

invalid JSON
missing fields
extra fields
wrong enum values
natural language around JSON
hallucinated data

Your application should handle these cases.

Retry with feedback

If validation fails, retry with a short correction prompt. Do not retry forever. Track validation failure rate by model and prompt.

Choose models carefully

Some models follow structured output instructions better than others. Evaluate models on schema pass rate, not just answer quality.

Final thoughts

Structured output makes LLMs useful in software workflows. Use schemas, validation, retries, and model-specific evaluation before relying on generated JSON in production.