OpenAI Structured Outputs: Guide & Examples

It’s messy and inefficient.

Structured outputs solve this.

They organize information in a predictable format, like JSON or lists.

This allows for easy parsing and use in other applications or systems.

Think about extracting data to populate a database, or to automatically fill in forms.

Structured outputs in openai makes these tasks straightforward.

How to Get Structured Outputs from OpenAI?

OpenAI’s API offers several ways to get structured data. Function calling is one powerful method.

With function calling, you describe the structure you want.

The model then returns a JSON object conforming to that structure.

This method is incredibly useful when you need specific information extracted in a defined format every time.

Another approach is to use prompt engineering.

By carefully crafting your prompts, you can guide the model to output data in a more structured, predictable way even without explicit function calls.

Examples of Structured Output Formats

Let’s look at some common structured output formats.

JSON Objects

JSON (JavaScript Object Notation) is a widely used format for structured data.

It’s human-readable and easily parsed by machines.

An example of a JSON output could be:

{
  "name": "London",
  "population": 9000000,
  "country": "UK"
}
  

This format is perfect for representing entities and their attributes.

Lists

Lists are another simple yet effective structured format.

They are ideal for outputting series of items.

For example, a list of top 5 movies could be:

1. The Shawshank Redemption
2. The Godfather
3. The Dark Knight
4. Pulp Fiction
5. 12 Angry Men

Lists are easy to process and present information clearly.

CSV (Comma Separated Values)

CSV is a popular format for tabular data. Each line represents a row, and values are separated by commas.

While OpenAI’s native output isn’t CSV, you can easily process list or JSON outputs to convert them to CSV if needed. This is useful for importing data into spreadsheets or databases.

Use Cases for OpenAI Structured Outputs

Here are a few examples:

• Data Extraction: Pulling specific details from web pages or documents into a database.
• Content Summarization: Condensing articles into key points in a structured bullet list.
• Product Information Retrieval: Fetching product specifications and details from online stores.
• Automated Reporting: Generating reports with key metrics organized in tables.
• API Integration: Feeding structured data into other APIs and services for seamless workflows, similar to how OpenAI Operator API can streamline processes.

These are just a few examples; the potential is limited only by your imagination.

Getting Started with Structured Outputs

Experimentation is key.

Start with simple prompts and function definitions to understand how OpenAI models respond.

Read the OpenAI API documentation to delve deeper into function calling and prompt engineering techniques.

Practice refining your prompts and function calls to achieve the exact structured output you need.

Conclusion

Structured outputs in openai are a game-changer.

They move beyond simple text generation and unlock powerful data processing capabilities.

By mastering techniques to get structured data, you can build sophisticated applications and automate complex tasks.