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To guarantee LLM outputs conform to a JSON schema, you can use finite-state machines and grammar-based generation to filter tokens during inference. This ensures 100% valid structured data without relying on post-processing validation.

Yes, you can use Pydantic models for structured generation with local models. The system applies type-safe, schema-driven validation during token selection across local backends like Transformers, llama.cpp, and vLLM.

The best way to prevent invalid JSON syntax is grammar-driven generation, which applies zero-overhead token filtering during inference. This constrains the model's output to match your exact schema or regex pattern instantly.

Yes, constrained generation works with vLLM and llama.cpp backends. It supports flexible deployment across local backends like Transformers, llama.cpp, and vLLM, as well as API endpoints, ensuring valid outputs across environments.

Structured generation reduces the need for re-prompts by enforcing schema compliance during the token selection process. By guaranteeing 100% valid outputs initially, it eliminates the post-processing validation failures that typically trigger re-prompting.

You need finite-state machines for LLM data extraction when you want to enforce strict structural patterns like JSON or regex during generation. This approach guarantees type-safe outputs for reliable form processing and data pipelines.