Prompt Engineering Patterns

关于

This skill provides a comprehensive framework for mastering prompt engineering within production environments. It enables developers to implement sophisticated reasoning patterns, manage dynamic few-shot example selection, and design modular prompt templates that ensure consistent and controllable AI behavior. By treating prompts as a core part of the codebase, it helps teams move beyond simple instructions to create robust LLM-driven applications with built-in error recovery, performance monitoring, and version control.

主要功能

• Systematic prompt optimization and performance tracking
• Dynamic few-shot learning and semantic example selection
• Robust error recovery and fallback instruction patterns
• Modular prompt template systems with variable interpolation
• Structured reasoning via Chain-of-Thought and verification steps
• 1 GitHub stars

使用场景

• Improving output consistency for complex reasoning and extraction tasks
• Scaling LLM prototypes into reliable production-grade services
• Optimizing token efficiency and reducing latency in high-volume applications