LLMInference & InterfacesUpdated 2026.04.28

Structured Output

Also known asJSON 모드JSON ModeSchema-constrained Output

In one line

Structured output forces an LLM to reply in a predefined JSON or schema shape instead of free text — essential when you need to plug AI reliably into other systems.

Going deeper

Structured output forces the LLM to reply in a predefined JSON or schema shape rather than free prose. OpenAI exposes it via 'json mode' and structured outputs; Anthropic does it through tool definitions. It is effectively required whenever AI output has to feed downstream systems.

For marketers running automation, this is the feature that decides whether the pipeline is reliable. Free-text answers break parsers; schema-shaped answers slash error rates and make follow-up processing trivial.

Watch out for over-tight schemas. If required fields cannot be filled, models either refuse or hallucinate values to fill the gap. Keep a clean split between required and optional fields, and mark genuinely nullable fields explicitly.

Related terms

LLM

Function Calling

Function calling is the interface that lets an LLM invoke predefined functions or APIs instead of just replying in natural language — the core mechanism behind AI agents.

LLM

Prompt Engineering

Prompt engineering is the practice of crafting inputs that steer an LLM toward better outputs — a way to dramatically change result quality without retraining the model.

LLM

RAG

RAG (Retrieval-Augmented Generation) lets an LLM fetch external documents at answer time and ground its response in them — the technique behind ChatGPT Search, Perplexity and most AI search products.

LLM

A large language model (LLM) is a neural network trained on massive text corpora to understand and generate human language — the engine behind ChatGPT, Claude, Gemini and similar products.

SEO

JSON-LD

JSON-LD (JSON for Linked Data) is the JSON-based format used to embed Schema.org markup in a page — currently Google's recommended way to ship structured data.

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