How to Implement Structured Output with Local LLMs

You can compel local large language models to generate predictable, structured output by defining a desired schema using a tool like Pydantic. This process constrains the model's generation, transforming its free-form text into a clean object your application can easily parse. However, even if the output's format is correct, the model can still make factual errors when handling complex schemas that require multiple reasoning steps at once. To improve accuracy and reduce these errors, it is best to decompose a complex request into smaller, sequential tasks for the model to handle.