Structured output is an LLM capability and interface pattern where the model must emit data that conforms to a predefined schema (e.g., JSON, XML, Pydantic/JSON Schema), often validated by the runtime. It improves reliability, safety, and downstream integration by replacing free‑form text with machine‑checkable fields, types, and enums.
What is Structured Output?
Structured output constrains generation to a target shape. The developer provides a schema—field names, types, cardinality, enums, regex, and descriptions. At inference, the model is prompted with the schema and examples, or guided by constrained decoding, to produce outputs that parse and validate. Runtimes enforce correctness by rejecting malformed objects, auto‑repairing with error messages, or re‑prompting with diffs. This approach pairs naturally with function calling, where arguments must match tool schemas, and with JSON Modes/grammar‑based decoding (EBNF/BNF) that limit token choices to valid continuations so the model cannot emit arbitrary text.
Why it matters and where it’s used
Structured outputs reduce hallucinations about format, enable deterministic pipelines, and simplify audits. They power agents that must pass arguments to tools, RAG systems that return cited answers with structured provenance, form/extraction workloads, analytics reports, and integrations that expect JSON over APIs, queues, or databases.
Examples
- Tool calls: emit {“tool”:”create_ticket”,”args”:{…}} with validated enums and ranges.
- Extraction: return a schema with entities, spans, confidence, and citations.
- Reporting: produce a typed object with KPIs, units, thresholds, and links.
- Safety: enforce allow/deny lists and required fields to prevent unsafe actions.
FAQs
- How is this different from function calling? Function calling is one concrete protocol for tool invocation; structured output is the broader pattern of enforcing schemas on any model response.
- Do I need special decoding? Constrained/grammar decoding increases validity; fallback parsers plus repair prompts also work.
- What about partial compliance? Validate and return structured errors so the model can self‑correct; cap retries.
- Can it reduce prompt injection risk? It helps contain outputs, but you must still isolate untrusted inputs and enforce tool scopes.
- Does strict structure hurt creativity? Use dual‑channel outputs: a structured summary for systems and a free‑text companion for humans.