Structured Output
Structured output is when a language model returns data in a predictable, machine-readable format—JSON, XML, typed objects—rather than free-form prose. This is what makes LLMs usable as components in software systems rather than just conversational interfaces. If you need the model to extract a name, date, and dollar amount from an invoice, you need those values in fields your code can parse, not embedded in a sentence. Most model providers now support constrained generation—forcing the model's output to conform to a JSON schema—which eliminates the parsing failures that plagued early integrations. OpenAI's structured output mode, Anthropic's tool use, and open-source libraries like Instructor all solve this problem. Structured output is the bridge between AI as a chat feature and AI as a system component, and getting it right is prerequisite to any serious automation.
Related terms:
Prompt Injection
Prompt injection is an attack where a user or data source inserts instructions that override a language model’s intended behavior.
Token
In large language models, a token is the basic unit of text—usually chunks of three to four characters—that the model reads and generates.
Model Context Protocol (MCP)
Model Context Protocol (MCP) is an open standard from Anthropic that standardizes how AI models connect to external tools and data sources via a...