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To guarantee valid JSON structured generation, this Skill constrains token sampling during inference using CFG and FSM filtering. It enforces Pydantic and JSON-schema constraints directly at the logit level, making invalid tokens impossible.

Yes, you can use Pydantic models to enforce type-safety with vLLM backends. The Skill compiles your schemas into FSMs for logit-level filtering, ensuring high-throughput inference produces outputs that map directly to your application models.

The best way to prevent malformed outputs during data extraction is schema-level constraint enforcement during generation. By filtering tokens against compiled grammars, it eliminates invalid JSON or XML structures without expensive post-validation and retries.

Yes, FSM token filtering works with Transformers and llama.cpp. It supports local and API model backends, compiling JSON-schema constraints to finite state machines to optimize latency and batching for zero-overhead inference.

You need FSM token filtering instead of post-validation because it guarantees valid JSON-schema compliance during generation. Constraining token sampling to grammars prevents malformed outputs instantly, eliminating the latency and cost of retrying failed validation attempts.