Prompt Engineering Patterns

About

The Prompt Engineering Patterns skill provides a comprehensive framework for building production-grade LLM applications using sophisticated prompting strategies. It enables developers to implement complex reasoning patterns such as Chain-of-Thought, manage dynamic few-shot learning systems, and create modular, version-controlled prompt templates. By moving beyond basic instructions to structured architectural patterns, this skill ensures AI outputs are consistent, cost-effective, and highly reliable for critical software tasks and automated workflows.

Key Features

• Dynamic few-shot learning with semantic example selection
• Structured reasoning elicitation using Chain-of-Thought and verification steps
• Reusable prompt template systems with variable interpolation
• Specialized system prompt design for role-based AI behavior
• 6 GitHub stars
• Iterative optimization workflows for token efficiency and latency

Use Cases

• Designing complex reasoning flows for production LLM applications
• Scaling AI systems with dynamic context and example retrieval
• Optimizing prompt consistency and reducing output variance