Structured Output

Structured Output refers to techniques for getting language models to return responses in specific, parseable formats like JSON, XML, or custom schemas. This is crucial for integrating AI into applications where downstream code needs to process model outputs reliably.

Methods for achieving structured output include: detailed format instructions in prompts, providing example outputs, using JSON mode (available in many APIs), specifying JSON schemas that the model must follow, function calling with defined return types, and post-processing with validation and retry logic.

Reliable structured output is essential for: building AI features in applications, creating data processing pipelines, implementing function calling, and any use case where model outputs must integrate with other systems. Engineers should understand the tradeoffs between different approaches and implement appropriate validation to handle edge cases where models don't perfectly follow formats.