Constrain LLM token generation with JSON, Pydantic, or regex grammars.
npx skills add https://github.com/devMoez/titan --skill outlines-devmoez
Please help me install this Agent Skill. Skill: outlines Source: https://github.com/devMoez/titan/tree/main/optional-skills/mlops/inference/outlines Command: npx skills add https://github.com/devMoez/titan --skill outlines-devmoez
This Skill solves the reliability problem of getting LLM outputs that strictly match a required structure, so you can use model results directly without brittle post-processing.
Use the outlines skill to extract a Product object from the prompt: “Extract iPhone 15 Pro details (name, price, in_stock) from: iPhone 15 Pro costs $999 and is currently in stock.”
High-intent search queries and answers about installing and using this skill.
Guarantee valid JSON generation by constraining LLM token sampling with grammar-based decoding. You define a Pydantic schema, and the constrained sampling mechanism ensures the output strictly matches that structure, yielding type-safe validated objects.
Yes, structured generation works with vLLM, Transformers, and llama.cpp backends. The grammar constraints integrate with these local inference engines to control latency and throughput while enforcing strict JSON, regex, or code output formats.
The best way to extract structured data without brittle post-processing is grammar-based decoding. By applying JSON or Pydantic schema constraints during generation, the LLM is forced to produce structurally valid, correctly typed outputs directly from messy text.
Yes, regex constrained decoding works for classification tasks by restricting token generation to valid regex patterns. This ensures the LLM output strictly matches allowed classification labels during local inference without requiring additional validation steps.
Grammar-based decoding is needed because prompt engineering only encourages structured outputs through best-effort text generation. Constrained sampling guarantees structural validity at the token level, ensuring outputs match your JSON schema or Pydantic model exactly.
The limitation of Pydantic model-to-schema integration is that it requires the outlines library and compatible local backends. Complex nested schemas may constrain generation heavily, potentially increasing latency while ensuring type-correct structured outputs.
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