Constrain LLM output to match regex patterns or grammars.
npx skills add https://github.com/DaddyElonMusk69/motis-agent --skill guidance-daddyelonmusk69
Please help me install this Agent Skill. Skill: guidance Source: https://github.com/DaddyElonMusk69/motis-agent/tree/main/skills/mlops/inference/guidance Command: npx skills add https://github.com/DaddyElonMusk69/motis-agent --skill guidance-daddyelonmusk69
LLMs often generate outputs that deviate from required formats, causing downstream parsing errors and increased latency. This Skill enforces syntactic constraints such as regex or grammars to guarantee valid JSON, XML, code, or other structured data.
Generate a JSON user profile with name and age using the guidance skill.
High-intent search queries and answers about installing and using this skill.
To constrain LLM output with regex or grammars, you use the guidance package to enforce syntactic constraints during generation. This guarantees the model produces valid structured formats like JSON or XML without downstream parsing errors.
Yes, the guidance package supports backend flexibility and works with OpenAI, Anthropic, HuggingFace Transformers, and llama.cpp models. This allows you to apply constrained generation across various compatible model backends.
LLMs generate invalid JSON due to unconstrained token prediction. By enforcing grammar or regex constraints with the guidance package, you guarantee valid JSON, XML, or code output and eliminate downstream parsing errors.
You generate structured data in multi-step pipelines by combining constrained generation with Pythonic control flow using the guidance package. This approach enforces regex or grammar patterns while orchestrating complex generation workflows.
Token healing is a feature that automatically fixes spacing issues at token boundaries during constrained generation. It ensures smoother text output when LLMs are forced to match regex patterns or grammars.
Using regex and grammars for constrained generation requires the specific guidance Python package and compatible model backends like OpenAI, Anthropic, or Transformers. It is limited to environments where these dependencies are installed.
SethyPagnaGenerate structured text using Python-based Guidance syntax with regex and grammar constraints.
MesferAliGenerate valid JSON, XML, and code using regex and grammars.
ovachieverConstrain LLM outputs to valid JSON, XML, and code using regex and grammar constraints.
afel6Constrain LLM generation with regex and grammar rules for valid JSON, XML, or code.
