OpenAI has introduced the Structured Outputs feature, which allows model developers to define specific schemas for language model outputs. This represents a shift back towards structured data requirements after a brief period of reliance on unstructured models. Language models historically produced non-deterministic outputs, complicating their integration with traditional systems that require structured data. The new approach provides a framework for ensuring that outputs can be aligned for practical applications, particularly in enterprise settings that demand consistency and accuracy. There is recognition that while creativity in outputs is valuable, a balance must be struck between innovation and safety, especially in enterprise use cases. The Structured Outputs feature facilitates this by allowing developers to impose constraints that enhance governance and control over outputs, addressing practical deployment issues faced by businesses. Furthermore, the announcement noted a shift in OpenAI's data retention policy for schemas, where previously established zero data retention principles are adjusted for unique value recognition in schema data. This adaptation demonstrates OpenAI's understanding of the need for structured outputs while highlighting the complexities surrounding enterprise governance and evolving product features. The overall emphasis is on making language models practical tools in corporate environments, ensuring that they can effectively communicate within heterogeneous systems while maintaining the required control over output data.