Constrain LLM outputs with regex and grammar constraints.
npx skills add https://github.com/AissenLiu/EasyHermes --skill guidance-aissenliu
Please help me install this Agent Skill. Skill: guidance Source: https://github.com/AissenLiu/EasyHermes/tree/main/hermes-agent/optional-skills/mlops/guidance Command: npx skills add https://github.com/AissenLiu/EasyHermes --skill guidance-aissenliu
Constrain LLM outputs with regex and grammars to ensure structured and valid results.
Configure Guidance with a grammar or regex constraint and run a simple example to generate a structured output.
High-intent search queries and answers about installing and using this skill.
You can constrain LLM outputs for valid JSON and XML generation by applying grammar-based constraints during inference. This ensures the generated text strictly adheres to your specified schema, preventing formatting errors.
The best way to validate structured formats like emails and dates is by applying regex-based constraints. This directly restricts the LLM token generation to match your defined regular expressions, guaranteeing valid format output.
Yes, constrained generation works with local LLM backends like Transformers and llama.cpp. The Guidance library supports applying regex and grammar constraints across API-backed and local backend environments for structured generation.
You can build multi-step workflows for reliable data extraction by combining grammar constraints with token healing. This approach repairs broken tokens at boundaries and structures sequential prompts to ensure robust end-to-end extraction.
Use token healing in structured LLM generation when prompt boundaries break tokens, causing invalid syntax. It repairs these boundaries before generation, ensuring regex and grammar constraints apply cleanly without tokenization errors.
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