Structured Outputs (2024) › The Problem

Before structured outputs, developers faced a fundamental challenge when trying to extract structured information from language model responses. Models generated natural language text that might contain the desired information, but the wording and layout varied between responses. A model might say "The user's email is john@example.com" in one response, "Email: john@example.com" in another, and "Contact john@example.com for more information" in yet another. While a human could easily extract the email address from any of these formats, programs required consistent, predictable formats that could be reliably parsed.

The variability in model outputs created several practical problems for developers. First, parsing natural language to extract structured data required writing complex regular expressions or natural language processing logic that could handle multiple formats. This parsing logic was brittle and prone to failure when the model's output format changed slightly. A small variation in wording or punctuation could break the parser, causing the application to fail. Developers had to write extensive error handling code to deal with the various ways models might format information.

Second, the lack of guarantees about output format made it difficult to validate model responses or to provide reliable APIs. If an application promised to return structured data to users, it could not guarantee that the language model would produce output in the expected format. This made it risky to build services that depended on model outputs for critical functionality. Applications had to include fallback logic and manual review steps, increasing complexity and reducing automation potential.

Third, the inconsistency in output formats made it difficult to chain multiple operations together reliably. If one step in a workflow produced unstructured text that needed to be parsed for the next step, parsing failures could break the entire workflow. Developers had to build extensive error handling and retry logic, making workflows complex and fragile. The lack of reliable structured outputs prevented language models from serving as reliable components in automated systems.

Fourth, for applications requiring specific data schemas or validation, the inability to guarantee structured outputs created significant challenges. If an application needed data that matched a specific JSON schema, XML format, or database schema, it could not rely on language models to produce outputs that would pass validation. Applications had to include post-processing steps that attempted to transform unstructured text into the required format, with varying success rates.

The problem extended beyond simple data extraction to more complex use cases. Applications that needed to extract multiple entities, classify content into categories, parse user intents, or structure information according to specific schemas all faced the same fundamental challenge: language models produced text, not structured data. Developers had to build custom parsing logic for each application, increasing development time and introducing potential points of failure.

Previous approaches to structured data extraction had limitations. Some developers used prompt engineering techniques to request specific formats, such as asking models to "return the data as JSON" or providing examples of the desired format. While these techniques could improve consistency, they did not provide guarantees. Models might still deviate from requested formats, produce invalid JSON, or include additional text that broke parsers. Other approaches used post-processing to parse model outputs, but these were brittle and could not handle the full variability of natural language generation.

The development of function calling in 2023 had addressed structured outputs for function calls specifically, but the broader problem of extracting structured data from model responses remained. Applications that needed structured data for purposes other than function calling still had to rely on unreliable text parsing. The need for a more general solution to structured outputs became increasingly apparent as developers built more sophisticated applications with language models.