Structured Output from LLMs

Structured Output from LLMs

Many times the purpose of LLMs output will be used to feed a program or system. While LLMs excel at generating natural language, getting them to reliably produce data in a specific format (JSON, XML, CSV, etc.) is more of an exercise in "iteration". If you have been dealing with creating structured output from your LLM or agents, using tools like Logit can provide a very clean way to modify the LLM outputs directly. This avoids failures with re-prompting.

This isn't just about pretty formatting – it's about making LLM-generated content directly usable by other systems, databases, or downstream processes. Think about automatically extracting key information from documents, populating forms, or generating API responses.

Try prompt engineering with clear schema definitions, using Pydantic or similar libraries.

Simple response class that has type safety, auto-validation, and serialization:

class QuestionResult(BaseModel): questionnumber: int question: str answer: Optional[str] = None status: str = "pending"# pending, success, error error: Optional[str] = None sources: List[Source] = Field(defaultfactory=list) query_time:Optional[float] = None