Constrain LLM outputs with regex and grammars using Guidance.
npx skills add https://github.com/peteromallet/megaplan --skill guidance-peteromallet
Please help me install this Agent Skill. Skill: guidance Source: https://github.com/peteromallet/megaplan/tree/main/megaplan/agent/skills/mlops/inference/guidance Command: npx skills add https://github.com/peteromallet/megaplan --skill guidance-peteromallet
Guidance provides robust mechanisms to constrain LLM outputs using regexes and grammars, guaranteeing structured and valid results across JSON, XML, code, and other formats.
Feed Guidance a set of constraints (regex or grammar) to generate a valid, structured output.
High-intent search queries and answers about installing and using this skill.
Constrain LLM outputs by applying regular expressions and context-free grammars to generation. This enforces structured formats like JSON and XML, guaranteeing predictable and valid results for data pipelines and automation tasks.
Use regex constraints during the generation process to force the LLM to follow specific patterns. This approach guarantees the output matches your defined regular expression, preventing formatting errors and invalid text.
Yes, you can build multi-step workflows and tool-augmented agents using Pythonic patterns. This allows you to chain constrained generation steps together for repeatable automation across local and API backends.
Yes, grammar-based constrained generation works across both local and API backends. This allows you to apply consistent structured output rules regardless of where your model is hosted.
LLMs often produce invalid structured data because tokenization breaks format instructions. Applying token healing alongside grammar-based generation corrects these tokenization issues and enforces strict format validation.
Use context-free grammars instead of regex when you need to enforce nested or recursive structures like balanced code blocks or complex XML. Regex is better suited for simpler, linear pattern matching constraints.
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