Schema

For years, extracting structured data from LLMs meant post-processing their text output: parse JSON, handle edge cases where the model forgot to close a bracket, write validation code to check if the output matched your schema, implement fallback logic when parsing failed. Then came structured outputs - a way to constrain LLM responses to match a JSON schema before they’re returned to you. Structured outputs sound simple but represent a fundamental shift in how to build production LLM systems. And yet, most teams are still extracting data the old way - waiting for the post-processing disasters that guaranteed outputs prevent. ...