Structured Output Generation

Structured output generation concerns responses such as JSON, tool calls, tables, and domain-specific records that downstream software can parse and use. Common approaches include schema-constrained decoding, prompt and schema design, fine-tuning or reinforcement learning with structural rewards, and validation-and-repair loops; some systems instead separate reasoning from deterministic formatting or compilation. Benchmarks increasingly measure structural validity separately from value accuracy, semantic correctness, and executable behavior across tasks such as extraction, question answering, and workflow generation. A central tension is that constraints can guarantee valid form but may reduce reasoning performance, especially in smaller models, while valid outputs can still contain incorrect or misplaced information.