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You can generate structured outputs by defining a Pydantic model or JSON schema, then passing it with your natural language prompt to constrain the model's response into validated, type-safe JSON.

Yes, structured prompt generation fully supports local model backends like Transformers, vLLM, and llama.cpp, enabling type-safe outputs and JSON schema validation without relying on online APIs.

The best way to extract data and validate it against a JSON schema is to use Pydantic models with CFG-based constraints, which enforce type-safe generation and automatic validation during the extraction process.

Yes, safe prompt handling mechanisms are applied during structured output generation to prevent the leakage of sensitive data when processing natural language inputs.

Using Pydantic models for generating API-ready payloads ensures outputs strictly conform to a predefined schema with automatic validation, eliminating ambiguity and reducing post-processing effort.