Constrain LLM outputs with regex and grammars for structured JSON, XML, or code generation.
npx skills add https://github.com/hhhi21g/HealthCenter --skill guidance-hhhi21g
Please help me install this Agent Skill. Skill: guidance Source: https://github.com/hhhi21g/HealthCenter/tree/main/.codex/skills/guidance Command: npx skills add https://github.com/hhhi21g/HealthCenter --skill guidance-hhhi21g
Guidance helps control and structure LLM outputs with regex and grammars, ensuring valid JSON/XML/code generation and structured formats.
Use the guidance skill with the following example: generate_person(name="John Doe", age=30, email="[email protected]").
High-intent search queries and answers about installing and using this skill.
To constrain LLM outputs for valid JSON generation, you can use regex constraints and grammar-based generation to enforce specific structured formats. This ensures the model produces syntactically correct structured data.
Grammar-based generation is a prompt engineering technique that controls LLM output syntax using defined grammars or regex patterns. It guarantees valid JSON, XML, or code generation by restricting the model to specific structural patterns.
You can extract structured data from unstructured text by applying regex constraints and multi-step workflows with Pythonic control flow. This approach ensures accurate information extraction and enforces the desired output format reliably.
Yes, you can use regex pattern matching to control LLM syntax and enforce structured formats. This capability allows you to restrict the generated content to specific patterns, improving the accuracy and reliability of the output.
kaminocorpGenerate constrained LLM outputs using regex, grammars, and token healing with Guidance.
cabeznoGenerate schema-constrained JSON, XML, and code with grammar-based token filtering.
NITISH-gitbitGenerate valid JSON, XML, code, and structured data using regex constraints and grammars.
EstomDesign and optimize prompts for LLM output quality and reliability.
