Schema-constrained generation and validation loops that make LLMs safe to build on.
Asking a model to reply in JSON and parsing the result works in testing and fails in production, usually at three in the morning on an unusual input.
Constrain generation rather than hope
Use the schema-constrained or tool-calling modes the provider offers, which restrict the tokens the model can produce so the output is valid JSON matching your schema by construction. This removes the entire class of trailing commas, markdown fences and explanatory prose wrapped around the object.
Validate anyway
Schema conformance is not correctness. A date field can be well-formed and impossible, an enum can be valid and wrong for the context, a required identifier can refer to nothing. Validate against your business rules after parsing, and treat a failure as a retry with the error fed back rather than as a crash.
Design the schema for the model
Flat structures with clearly named fields work better than deeply nested ones. Give every field a description, use enums instead of free text wherever the set is known, and allow an explicit unknown value so the model has an honest option other than inventing one.
Log every validation failure with the input that caused it. That log is the highest-value evaluation set you will build.
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