Structured output from LLMs is the practice of constraining a large language model to produce responses in a predefined format — such as JSON or a specific programming class. This technique is essential for building reliable, production-grade applications, transforming the LLM's typically unpredictable text into a machine-readable, deterministic data contract. Based on successful production use, we’re moving this technique from Assess to Trial.
Approaches range from simple prompt-based formatting and model-native structured outputs to more robust constrained decoding methods using tools like Outlines and Instructor, which apply finite-state machines to guarantee valid output. We’ve successfully used this technique to extract complex, unstructured data from diverse document types and convert it into structured JSON for downstream business logic.
Structured output from LLMs refers to the practice of constraining a language model's response into a defined schema. This can be achieved either by instructing a generalized model to respond in a particular format or by fine-tuning a model so it "natively" outputs, for example, JSON. OpenAI now supports structured output, allowing developers to supply a JSON Schema, pydantic or Zod object to constrain model responses. This capability is particularly valuable for enabling function calling, API interactions and external integrations, where accuracy and adherence to a format are critical. Structured output not only enhances the way LLMs can interface with code but also supports broader use cases like generating markup for rendering charts. Additionally, structured output has been shown to reduce the chance of hallucinations in model outputs.
Structured output from LLMs refers to the practice of constraining a language model's response into a defined schema. This can be achieved either through instructing a generalized model to respond in a particular format or by fine-tuning a model so it "natively" outputs, for example, JSON. OpenAI now supports structured output, allowing developers to supply a JSON Schema, pydantic or Zod object to constrain model responses. This capability is particularly valuable for enabling function calling, API interactions and external integrations, where accuracy and adherence to a format are critical. Structured output not only enhances the way LLMs can interface with code but also supports broader use cases like generating markup for rendering charts. Additionally, structured output has been shown to reduce the chance of hallucinations within model output.
