Constrain AI-generated text to structured formats using regex and grammars.
npx skills add https://github.com/lenadlm/docker --skill guidance-lenadlm
Please help me install this Agent Skill. Skill: guidance Source: https://github.com/lenadlm/docker/tree/main/hermes-skills/skills/mlops/inference/guidance Command: npx skills add https://github.com/lenadlm/docker --skill guidance-lenadlm
This Skill allows users to control the output of AI-generated text using regex, grammars, and structured output formats, ensuring accuracy and consistency.
To use the guidance skill, load a model and specify the constraints. For example:
from guidance import models, gen
lm = models.OpenAI("gpt-4")
result = lm + "The capital of France is " + gen("capital", max_tokens=5)
print(result["capital"]) # "Paris"
High-intent search queries and answers about installing and using this skill.
You can constrain LLM output to specific formats like JSON or XML by applying regex patterns and grammars during text generation. This enforces structured outputs, ensuring the model produces syntactically valid results for automated code generation and data extraction.
Constrained generation restricts AI models to specific grammars or regex patterns, streamlining prompting and limiting output scope. This structured approach reduces latency by preventing invalid generations and minimizing repeated parsing or retries.
Yes, you can use regex to control AI text generation with compatible backends like OpenAI, Anthropic, or llama.cpp. By loading a model and specifying constraints, you enforce formatting rules directly during the generation process.
Yes, you need the guidance library along with dependencies like transformers or llama_cpp to generate structured output. It provides the framework to define grammars and regex constraints that control model behavior and enforce formats.
The best way to ensure valid syntax for automated code generation is using constrained generation with grammars. By defining strict structural rules, you prevent syntax errors and ensure the AI-generated code adheres to required programming language specifications.
Structured output processing often fails without grammars or regex constraints because standard LLMs can deviate from required formats. Applying constraints forces the model to adhere to defined structures, preventing parsing errors during document processing.
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