Generate valid JSON, XML, and code using regex and grammatical constraints.
npx skills add https://github.com/superfhp/lumi-agent --skill guidance-superfhp
Please help me install this Agent Skill. Skill: guidance Source: https://github.com/superfhp/lumi-agent/tree/main/optional-skills/mlops/guidance Command: npx skills add https://github.com/superfhp/lumi-agent --skill guidance-superfhp
This Skill provides precise control over Large Language Model (LLM) outputs by utilizing regex constraints and grammars, allowing users to generate valid JSON, XML, code, and structured data with ease.
Use the guidance skill to generate a JSON object with a name, age, and email:
name: John Doe
age: 30
email: [email protected]
High-intent search queries and answers about installing and using this skill.
To ensure valid JSON generation from an LLM, you apply regex constraints and grammars to restrict the output space. This enforces structured formats, guaranteeing the model produces syntactically correct JSON without hallucinated fields.
Yes, you can use regex patterns to constrain specific fields during structured data generation. This allows you to enforce precise formats for text elements like names, ages, and emails, preventing invalid outputs from the LLM.
Yes, grammatical constraints support multi-step workflows by utilizing Pythonic control flow. This enables you to build complex, multi-step processes that reliably generate structured data while maintaining predictable results throughout the execution.
Before constraining LLM outputs, you need the guidance library and a compatible LLM backend installed in your environment. These dependencies are required to interpret the grammatical constraints and execute the structured generation process.
The best way to guarantee valid XML generation is to apply grammatical constraints that control the LLM output. This prevents invalid formatting and enforces the required structured XML tags directly during the generation phase.
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