Term
Structured Output / JSON Mode
Structured output is a language model's ability to deliver responses in a defined schema (typically JSON), reliable enough to be consumed directly by downstream code.
Structured Output / JSON Mode — explained in more detail
Free-text answers are hard for programs to parse. Structured output solves this by either forcing the model into schema conformance (constrained decoding against a JSON schema) or by having the model respond with tool calls whose arguments must follow a defined type. The result: the model never wraps the JSON in “Here is an example: …” but emits valid, parseable JSON directly.
Example / Practical context
A lead-capture flow extracts fields like name, company, phone, request_type from a free-form email. Without structured output you have to harden your parsing with regex; with JSON-schema mode the API hands back a type-safe object. Providers such as OpenAI and Anthropic and local engines (vLLM, llama.cpp via grammars) support this natively — either via a JSON-schema parameter or via tool use with a defined input schema.
Distinction from related concepts
Function calling and tool use are closely related: the model calls a function with structured arguments — mechanically the same as JSON mode, with the extra step of routing the output into an actual function. Few-shot prompting with JSON examples reaches similar results but is not enforced — the model can break out of the format at any time.
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GlossarEnsemble / Multi-Model Orchestration
Ensemble means combining several deliberately varied LLM runs or models whose findings complement each other. Multi-model orchestration drives these runs via orchestrators with sub-agents, so the union of results is larger than any single run.
LexikonFunction Calling / Tool Use
How an LLM uses tools: define a tool as a schema, the model picks the function and arguments, the result returns to the chat — the basis of every agent.