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Wiki AI Concepts

Structured Output

AI Concepts
Aliases： Structured Output JSON Mode JSON Schema Type-Safe Output ·2026-09-19

Structured Output

Structured output is the capability to make an LLM emit values that conform to a predefined schema (usually JSON), rather than free-form text. It turns the LLM from "a text generator that talks" into "a programmable function call" — downstream code no longer has to do brittle string parsing and retries.

Three mainstream implementations

1. JSON Mode (early)

  • Model is constrained to emit only valid JSON strings
  • Field names / types are not enforced — unexpected keys can still appear
  • OpenAI introduced it in 2023, Anthropic and Google followed

2. Structured Outputs (strict schema)

  • Caller provides a JSON Schema or similar DSL
  • Model is forced to follow the schema; type-mismatched output simply won't generate
  • OpenAI shipped it in August 2024 (GPT-4o); Anthropic followed with stricter tool-use in October 2024

3. Function Calling / Tool Use

  • "Structured output" repackaged as "tool call"
  • Model emits {name, arguments} where arguments is structured JSON
  • Native to Anthropic, OpenAI, Google, xAI, Mistral
  • Combined with MCP (Model Context Protocol) it became the de-facto agent tool-call standard

Why it matters

  • Directly consumable by code: instead of "Is this email a complaint?" returning a paragraph, the model returns {category: "complaint", confidence: 0.92} and downstream code just branches
  • Eliminates parsing failures: no more handling {"answer": "..."} vs Answer: ... vs markdown-wrapped JSON
  • Cuts retry cost: a malformed output used to mean a full retry; a schema mismatch now produces a typed error
  • Auditable: every output is a structured value that can land in version control / databases

The extreme form: System One Models

TypeSafe AI Jev pushes structured output to its logical extreme:

  • No string-generation phase at all; choices are enumerated in the schema up front
  • Every option comes with a calibrated probability
  • 0% type errors (a structural guarantee, not a statistical rate)
  • End-to-end 70-500 ms — 40-200× faster than LLMs

See typesafe-ai.md.

Evaluation

  • Schema compatibility: 100% (TypeSafe Jev) / 99.x% (GPT-4o structured)
  • Field accuracy: depends on prompt design and schema complexity
  • Hallucination boundary: structured output guarantees shape, not substance — Jev can still pick the wrong option, which is why probabilities matter

Common deployment scenarios

  • Data extraction (contracts, invoices, resumes)
  • Classification / routing / scoring
  • Agent tool calls (MCP)
  • Real-time decisioning (structured if-statements)
  • Table / database writes