Prompt Engineering

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Provides a comprehensive framework for designing, optimizing, and validating LLM prompts across various complexity levels. It guides users through a collaborative development process that includes deep exploration of needs, strategy selection—such as Chain-of-Thought or Few-Shot Learning—iterative implementation of minimal viable prompts, and rigorous validation against quality criteria. This skill serves as a sophisticated thought partner for improving the reliability, clarity, and efficiency of model interactions by applying proven engineering principles to the art of prompting.

Características Principales

• Comprehensive validation framework to identify and fix failure modes
• Task classification for selecting optimal prompting strategies
• Implementation of advanced techniques like Chain-of-Thought and Few-Shot Learning
• Structured workflow for prompt exploration and analysis
• 0 GitHub stars
• Iterative development process from MVP to high-performance versions

Casos de Uso

• Optimizing existing prompts to fix inconsistencies or improve response quality
• Translating vague user requests into actionable, high-quality prompts
• Applying specialized prompting patterns for complex multi-step reasoning tasks