Constrain LLM outputs with regex and grammars for valid JSON and XML.
npx skills add https://github.com/Monjyu1101/AiDiy2026 --skill guidance-monjyu1101
Please help me install this Agent Skill. Skill: guidance Source: https://github.com/Monjyu1101/AiDiy2026/tree/main/backend_hermes/optional-skills/mlops/guidance Command: npx skills add https://github.com/Monjyu1101/AiDiy2026 --skill guidance-monjyu1101
Automates the production of structured and validated outputs from LLMs by applying regex constraints, grammars, and multi-step workflows.
Generate a small JSON object with fields name, email, and age that conform to regex patterns and return valid JSON.
High-intent search queries and answers about installing and using this skill.
You can constrain LLM outputs to guarantee valid JSON generation by applying regex constraints and grammars during the generation process. This ensures the model produces structured data that strictly conforms to your required JSON schemas and API payloads.
Yes, you can use regex-guided generation to extract specific data formats like emails, dates, and IDs from LLM outputs. This approach forces the model to produce text that exactly matches your defined regex patterns for reliable data extraction.
Grammar-based generation works for complex structures like XML by defining structural rules that the LLM must follow during text generation. This approach also utilizes token healing to repair partial tokens, ensuring the final output forms valid and complete hierarchical structures.
Yes, you need a Python environment along with the Guidance library to use grammars for constrained generation. You can also configure optional local backends or transformers if you require offline usage for your workflows.
The best way to orchestrate multi-step workflows with LLM validations is using Pythonic control flow to sequence tool calls and checks. This allows you to automate the production of structured outputs while applying regex and grammar rules at each step.
LLM outputs fail to conform to JSON schemas when generation is unconstrained, allowing random token sequences. Applying grammar-based generation with token healing forces the model to strictly follow your schema rules, guaranteeing valid JSON and XML formatting.
SriRamkunamsettyGenerate schema-constrained JSON, XML, or code outputs from local LLM backends.
peteromalletConstrain LLM outputs with regex and grammars using Guidance.
ldzhhxxGenerates structured JSON, XML, or code outputs using regex and grammars constraints.
Z43LEnforce regex and grammar constraints for structured LLM outputs in Python.
