Getting Started With Openai Structured Outputs Datacamp

Emily Johnson

-Mar 12, 2026, 12:08 PM

getting started with openai structured outputs datacamp

Access to this page requires authorization. You can try signing in or changing directories. Access to this page requires authorization. You can try changing directories. Structured outputs make a model follow a JSON Schema definition that you provide as part of your inference API call. This is in contrast to the older JSON mode feature, which guaranteed valid JSON would be generated, but was unable to ensure strict adherence to the supplied schema.

Structured outputs are recommended for function calling, extracting structured data, and building complex multi-step workflows. You can use Pydantic to define object schemas in Python. Depending on what version of the OpenAI and Pydantic libraries you're running you might need to upgrade to a newer version. These examples were tested against openai 1.42.0 and pydantic 2.8.2. If you are new to using Microsoft Entra ID for authentication see How to configure Azure OpenAI in Microsoft Foundry Models with Microsoft Entra ID authentication. JSON is one of the most widely used formats in the world for applications to exchange data.

Structured Outputs is a feature that ensures the model will always generate responses that adhere to your supplied JSON Schema, so you don’t need to worry about the model omitting a required key, or... Some benefits of Structured Outputs include: In addition to supporting JSON Schema in the REST API, the OpenAI SDKs for Python and JavaScript also make it easy to define object schemas using Pydantic and Zod respectively. Below, you can see how to extract information from unstructured text that conforms to a schema defined in code. Structured Outputs is available in our latest large language models, starting with GPT-4o. Older models like gpt-4-turbo and earlier may use JSON mode instead.

In the realm of AI-driven applications, ensuring consistent and predictable outputs is paramount. OpenAI’s introduction of Structured Outputs addresses this need by allowing developers to define the exact format of the model’s responses, ensuring they adhere to specified schemas. Structured Outputs enable developers to constrain the model’s responses to a predefined structure, typically defined using JSON Schema. This ensures that the outputs are not only valid JSON but also match the expected format, reducing the need for post-processing and error handling. 🔧 Using pydanticmodel with text_format under the method called client.responses.parse Step 1: Define the pydantic model in models/document_extraction.py

Step 2: Create a main.py and run the following code. You will have the structured output in JSON format. Master OpenAI Structured Outputs: JSON mode, function calling, and response formatting. Reliable data extraction with guaranteed schema compliance. GPT-5.2 has fundamentally changed how we think about structured outputs. With the new Context-Free Grammar (CFG) engine, the model literally cannot generate tokens that violate your schema - it's not a suggestion, it's an enforcement mechanism at the token generation level.

Combined with the Responses API (the new open-source standard replacing Assistants API) and the Agents SDK for multi-agent workflows, structured outputs have evolved from a reliability feature into the type system for AI-powered applications. The paradigm has shifted to "Schema-First Development." Rather than prompting for JSON and hoping, you define schemas in Zod (TypeScript) or Pydantic (Python) first, then build prompts around them. GPT-5.2's "Thinking" mode reduces hallucinations by 30%, and complex JSON reliability has jumped from ~82% in 5.1 to over 92% in 5.2. The 16k output token limit for structured responses enables extraction of complex nested documents that were previously very difficult. This guide covers production patterns for 2026. Structured Outputs is an OpenAI feature that constrains model responses to match a predefined JSON schema.

When enabled, the model cannot produce output that violates your schema, meaning every required field will be present, every type will be correct, and every enum value will be valid. This is fundamentally different from asking the model to produce JSON in your prompt, which only works most of the time. With structured outputs, schema compliance is enforced at the API level. The evolution toward structured outputs reflects a maturing understanding of how LLMs fit into software systems. Early integrations treated models as text generators, parsing their output with regex and hoping for consistency. JSON mode improved reliability by ensuring valid JSON syntax, but schemas could still vary between calls.

Structured outputs represent the final step: the model becomes a typed function that accepts natural language and returns predictable data structures. In this guide, let's walk through how to get started with OpenAI's Structured Outputs with a practical example. OpenAI recently introduced Structured Outputs in their API, solving a key challenge that unlocks many new use cases. In this guide, let's look at what structured outputs are, how to get started with them, and an example use case. OpenAI had previously released JSON mode, which was a useful building block that allowed you to reliably output JSON from the API. The issue with JSON mode is that it didn't always guarantee that the model would conform to a specific, developer-provided schema.

In other words, you can use Structured Outputs to reliably generate structured data from unstructured inputs. While this was somewhat feasible before with prompting and parsing responses, the reliability wasn't always there. Now, with Structured Ouputs, you can build AI agents that are much more flexible and reliable and output responses in the exact format you need. With their evals, OpenAI shows that Structured Outputs scores a perfect 100% in producing complex JSON schema...not bad. This section provides examples of common use cases for working with OpenAI Structured Outputs using the openai-structured library. Extract structured movie reviews using OpenAI Structured Outputs with streaming:

Analyze code using OpenAI Structured Outputs with custom rules and streaming: Configure buffer settings for different OpenAI Structured Outputs use cases: Use different models with version validation: OpenAI’s Structured Outputs fundamentally change how developers build reliable applications on top of large language models. Instead of coaxing models with elaborate prompts to “return valid JSON,” you can now guarantee that responses conform to a precise JSON Schema or typed model, drastically reducing parsing errors, retries, and brittle post-processing.[1][2][7] This article explains very detailed structured outputs with OpenAI: what they are, how they differ from older patterns (like plain JSON mode), how to design robust schemas, integration patterns (Node, Python, Azure OpenAI, LangChain,...

Structured Outputs are an OpenAI API feature that ensures model responses always match a supplied JSON Schema, or equivalent type definition, when strict: true is enabled.[1][2][7] This is unlike earlier approaches where you had to parse free-form text or rely only on “valid JSON” promises. OpenAI’s evolution of output control can be summarized as: Learn the basics of structured LLM outputs with Instructor. This guide demonstrates how to extract consistent, validated data from language models.Large language models are powerful, but extracting structured data can be challenging.Structured outputs solve this by having LLMs return data in consistent, machine-readable...

Getting Started With Openai Structured Outputs Datacamp

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Step 2: Create A Main.py And Run The Following Code.