Constrain LLM output with regex and grammars for valid JSON, XML, and code.
npx skills add https://github.com/choice5346/BiSHE --skill guidance-choice5346
Please help me install this Agent Skill. Skill: guidance Source: https://github.com/choice5346/BiSHE/tree/main/.github/skills/guidance Command: npx skills add https://github.com/choice5346/BiSHE --skill guidance-choice5346
This Skill provides fine-grained control over Large Language Model (LLM) output, ensuring structured, valid, and predictable responses through constrained generation techniques.
Use the guidance skill to generate a valid JSON object for a user profile.
High-intent search queries and answers about installing and using this skill.
To guarantee valid JSON output from an LLM, use constrained generation with regex and grammars to enforce specific formats. This ensures structured, predictable responses and prevents errors in downstream processing without relying on traditional prompting.
Constrained generation is a technique that provides fine-grained control over LLM output by using regex and grammars. It enforces structured formats for dates, emails, and IDs, ensuring responses are valid and predictable.
Constrained generation supports various backends including OpenAI, Anthropic, and local models via Transformers and llama.cpp. This allows you to enforce structured formats across different platforms and environments.
Build multi-step workflows with structured LLM output by using grammars to enforce specific formats at each step. This facilitates creating complex conversational agents and reasoning chains while reducing latency compared to traditional prompting.
The best way to enforce specific formats like XML from an LLM is using constrained generation techniques with regex and grammars. This guarantees valid, structured output and reduces latency compared to traditional prompting methods.
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