quality-enriched-prompting

What problem does it solve?

This Skill addresses the challenge of systematically improving prompt quality for language models by applying principles from category theory, specifically [0,1]-enriched categories, to create a quantifiable and optimizable framework for prompt engineering.

Core Features & Use Cases

• Quality Metrics: Defines multi-dimensional quality vectors (clarity, specificity, completeness, coherence, relevance) for prompts and responses.
• Enriched Category Framework: Implements enriched categories where morphisms represent quality scores, enabling composition and analysis of prompt transformation quality.
• LLM-Based Evaluation & Improvement: Leverages LLMs to evaluate prompt quality and suggest improvements based on identified weak dimensions.
• Use Case: Enhance the quality of prompts used for generating technical documentation by iteratively refining them to maximize clarity, specificity, and relevance, ensuring the output is accurate and useful.

Quick Start

Use the quality-enriched-prompting skill to optimize the prompt 'Write a summary of the document.' for clarity and specificity.