When we use AI inside an automation flow, it’s common to need the response in order to decide the next step in the process. Many developers try to solve this by asking in the prompt for a specific format (for example: “reply only in JSON” or “give me just the number”). “reply only in JSON” “give me just the number” The problem is that, in practice, the model often adds extra “fluff” or generates something ambiguous. This breaks the structure that the code was expecting and ends up being more of a headache than a solution. Structured Output, a feature provided by OpenAI, solves exactly this problem: it allows you to define the format of the response. This way, the model is required to return in the expected format (for example, valid JSON), and it can also validate data types or restrict responses to a set of options. Structured Output 1. What is Structured Output? 1. What is Structured Output? Structured Output is a feature of the OpenAI API that forces the model to respond n a predefined format. This goes beyond simply instructing it in the prompt: If you only ask for a format, the model might still add extra comments or break the structure. With Structured Output, the API validates types and structure, guaranteeing consistency. If you only ask for a format, the model might still add extra comments or break the structure. ask With Structured Output, the API validates types and structure, guaranteeing consistency. So, if you ask for a field to be an integer, the model won’t be able to return a string. 2. How it works in practice 2. How it works in practice In the API call, you define a schema with required fields, data types, and even fixed options. Simple example for classifying the sentiment of a text: { "name": "analyze_sentiment", "description": "Classify the sentiment of a text as positive, negative, or neutral", "schema": { "type": "object", "properties": { "sentiment": { "type": "string", "description": "Sentiment classification of the text", "enum": ["positive", "negative", "neutral"] } }, "required": ["sentiment"] } } { "name": "analyze_sentiment", "description": "Classify the sentiment of a text as positive, negative, or neutral", "schema": { "type": "object", "properties": { "sentiment": { "type": "string", "description": "Sentiment classification of the text", "enum": ["positive", "negative", "neutral"] } }, "required": ["sentiment"] } } This schema ensures the output will always look like: { "sentiment": "positive" } { "sentiment": "positive" } or one of the other options defined. Here’s a more complex example in Python using the Agents SDK: class Order(BaseModel): ticker: str qty: int price: float = Field(..., ge=0.0) class AiDecision(BaseModel): daily_summary: str orders: list[Order] explanation: str model = model or os.getenv("OPENAI_MODEL", "gpt-5-mini-2025-08-07") agent = Agent(name="Assistant", output_type=AiDecision) result = Runner.run_sync(agent, prompt) return result.final_output class Order(BaseModel): ticker: str qty: int price: float = Field(..., ge=0.0) class AiDecision(BaseModel): daily_summary: str orders: list[Order] explanation: str model = model or os.getenv("OPENAI_MODEL", "gpt-5-mini-2025-08-07") agent = Agent(name="Assistant", output_type=AiDecision) result = Runner.run_sync(agent, prompt) return result.final_output The response already comes structured as a dict, with no need for extra parsing or validation: daily_summary='Today’s closing prices show ...' orders=[] explanation='No trades recommended today...' daily_summary='Today’s closing prices show ...' orders=[] explanation='No trades recommended today...' 3. Benefits for developers and companies 3. Benefits for developers and companies The main benefit is avoiding parsing or formatting errors, since there’s no risk of the model returning information outside the expected pattern. This makes integration with downstream systems much easier and increases the robustness and reliability of AI responses, even in complex use cases. 4. Practical applications 4. Practical applications One practical use I’m working on today, in a personal project, is a finance assistant that suggests which orders should be executed based on daily events. However, I’ve also seen strong use cases in: Report generation Email classification Document and text analysis Report generation Email classification Document and text analysis All of this is only possible when you can guarantee the AI’s output. Otherwise, reliability is drastically reduced, and the AI becomes more of a problem than a tool. 5. Limitations and cautions 5. Limitations and cautions Although Structured Output guarantees the format, the values themselves may not always make sense and can still suffer from the well-known AI problem of hallucination. Even so, by simply ensuring the correct format, many problems are solved. Other issues can usually be mitigated through prompt design and other model settings. Conclusion Conclusion Structured Output is an important step toward making AI more predictable and better integrated with real systems. It reduces the risk of errors, streamlines developers’ work, and increases trust for companies relying on automation. Over time, this feature is likely to become standard: AIs that are less of a black box and more focused on delivering useful, consistent, and integrable data.
