What problem does it solve? Language models often produce malformed JSON or free-form text that breaks downstream parsing pipelines. This Skill guarantees structurally valid outputs by constraining token generation at the logit level using finite state machines derived from your schema. ## Core Features & Use Cases - Schema-Constrained Generation: Convert Pydantic models, JSON schemas, regex patterns, or choice lists into grammars that filter invalid tokens during generation. - Multiple Model Backends: Run structured generation on Transformers, llama.cpp GGUF models, vLLM for high-throughput serving, or OpenAI APIs. - Type-Safe Outputs: Receive validated Pydantic objects with enforced constraints like field patterns, numeric ranges, enums, and nested models. - Use Case: Extract structured company information (name, founded year, industry, employee count) from hundreds of unstructured text documents into validated Pydantic objects for a database pipeline, with zero parsing failures. ## Quick Start Ask the AI to use Outlines with a Pydantic model to extract structured fields from your text and return a validated JSON object.
