Structured Data for LLMs: Schemas, Formats, and Validation
Structured data is crucial for reliable LLM applications, moving beyond ambiguous inputs and outputs to enable monitoring, auditing, and governance. It’s not just about getting JSON-shaped text; true structured data requires machine-readable fields under a consistent schema with defined constraints (like types, enums, and required fields). Without schema validation, ‘JSON-shaped text’ can lead to brittle systems. JSON Schema is a declarative language for specifying these constraints and is essential for validating LLM outputs. Tools like LangChain’s structured output feature leverage schemas (JSON Schema or Zod) to ensure agents return predictable, typed data, whether through tool calling or native model support. Implementing structured data involves designing schemas LLMs can follow, choosing appropriate formats (JSON, JSONL, CSV, etc.), and operationalizing them with pipelines and storage. This approach mitigates common LLM failures caused by ambiguous data handling.
