Structured Output
Model responses that conform to a predefined schema such as JSON, XML, or typed objects rather than free-form text. Structured output ensures AI responses can be reliably parsed and consumed by downstream application code.
Structured output solves one of the biggest pain points in building AI-powered applications: reliably extracting data from model responses. Instead of parsing free text with regex or hoping the model follows formatting instructions, structured output mode guarantees the response matches your specified schema. Most providers implement this through constrained decoding that only generates valid tokens.
For engineering teams, structured output is essential for any workflow where AI output feeds into application logic. Extracting entities from documents, classifying support tickets, generating product recommendations, or populating form fields all require predictable output formats. Without structured output, you need defensive parsing code, retry logic for malformed responses, and fallback handling. With it, you get type-safe AI outputs that integrate cleanly with your application. Always define the narrowest schema that meets your needs, as overly flexible schemas give the model room to produce unhelpful variations.
Related Terms
Model Context Protocol (MCP)
An open standard that defines how AI models connect to external tools, data sources, and services through a unified interface. MCP enables agents to dynamically discover and invoke capabilities without hardcoded integrations.
Tool Use
The ability of an AI model to invoke external functions, APIs, or services during a conversation to perform actions beyond text generation. Tool use transforms language models from passive responders into active problem solvers.
Function Calling
A model capability where the AI generates structured JSON arguments for predefined functions rather than free-form text. Function calling provides a reliable bridge between natural language understanding and programmatic execution.
Agentic Workflow
A multi-step process where an AI agent autonomously plans, executes, and iterates on tasks using tools, reasoning, and feedback loops. Agentic workflows go beyond single-turn interactions to accomplish complex goals.
ReAct Pattern
An agent architecture that interleaves Reasoning and Acting steps, where the model thinks about what to do next, takes an action, observes the result, and repeats. ReAct combines chain-of-thought reasoning with tool use in a unified loop.
Chain of Thought
A prompting technique that instructs the model to break down complex problems into sequential reasoning steps before producing a final answer. Chain of thought significantly improves accuracy on math, logic, and multi-step tasks.