guidance

Constrain LLM outputs with regex and grammars for structured JSON/XML generation.

Updated Aug 23, 2026
One-click install
npx skills add https://github.com/AVOI-CEO/avoi-agent --skill guidance-avoi-ceo
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: guidance
Source: https://github.com/AVOI-CEO/avoi-agent/tree/main/optional-skills/mlops/guidance
Command: npx skills add https://github.com/AVOI-CEO/avoi-agent --skill guidance-avoi-ceo

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

Regex and grammar constraints to control LLM outputs, guaranteeing structured formats like JSON/XML and preventing invalid or unsafe generations.

Core Features & Use Cases

  • Regex-based constraints enforce formats for emails, dates, IDs, and other fields.
  • Grammar-based generation enables CFG-driven structured outputs such as JSON, XML, and code.
  • Multi-step workflows orchestrate constrained prompts with token healing and validation for reliable pipelines.
  • Use Case: Build a validation layer that always returns a schema-compliant JSON document from user input.

Quick Start

Prompt an LLM to generate a JSON object that matches a given schema using regex and grammar constraints

Frequently Asked Questions about guidance

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I constrain LLM outputs to generate valid JSON?▼

Constrain LLM outputs using grammar-based generation to enforce schema-compliant JSON. Apply CFG-driven constraints and built-in token healing to guarantee deterministic, structured results without invalid tokens.

What is regex-based constrained generation for language models?▼

Regex-based constrained generation applies pattern matching to enforce specific formats like emails, dates, and IDs during text generation. This ensures deterministic outputs that match predefined constraints.

Can I use grammar constraints to extract structured data from unstructured text?▼

Yes, grammar-based generation enables structured data extraction by applying CFG constraints to outputs. Built-in validation checks ensure extracted data matches required formats like JSON or XML.

How do I prevent invalid tokens when generating XML from an LLM?▼

Prevent invalid tokens using built-in token healing and grammar checks during generation. Apply grammar-based constraints to enforce XML schema compliance and automatically validate outputs.

Does constrained generation work with local and hosted models?▼

Constrained generation applies to both local and hosted models. Regex and grammar constraints enforce deterministic outputs across environments, ensuring structured results for multi-step workflows.

What is the best way to build a validation layer for LLM JSON output?▼

Build a validation layer using grammar-based constraints to enforce JSON schema compliance. Combine regex constraints with token healing and automatic validation to guarantee valid structured outputs from user input.