Schema Prompting

Schema prompting means handing the model an explicit output schema — a JSON Schema, a TypeScript type, or just a labeled field list with types — and instructing it to return data that conforms exactly. Instead of hoping the model formats things usefully, you define the contract up front: field names, types, which are required, allowed enum values, and nesting. This is the reliability backbone for any AI feature whose output feeds downstream code rather than a human reader — extraction, classification, form-filling, tool arguments. It pairs with, but isn't the same as, provider features: json-mode guarantees valid JSON syntax, and constrained decoding or "structured outputs" can enforce the schema at the token level, whereas schema prompting is the prompt-side instruction that describes the shape. Best practice for SaaS builders: keep the schema minimal (every extra field is another chance to hallucinate), state that fields with no data should be null rather than invented, and always follow generation with output validation that parses and checks the result before you trust it.