Prompt Engineering Patterns

소개

This skill provides a comprehensive framework for mastering prompt engineering in production environments. It equips developers with high-level patterns for improving LLM reliability, controllability, and reasoning capabilities. By implementing structured techniques such as dynamic few-shot example selection, modular template systems, and iterative optimization workflows, users can reduce token usage, minimize hallucinations, and ensure consistent output quality across complex AI applications.

주요 기능

• Chain-of-thought reasoning implementation
• 0 GitHub stars
• Modular prompt template systems with variable interpolation
• Few-shot learning with semantic similarity selection
• Specialized system prompt design for AI assistants
• Performance optimization and token reduction strategies

사용 사례

• Building production-grade LLM applications that require high output consistency
• Implementing complex reasoning steps for technical or mathematical tasks
• Optimizing token efficiency and reducing costs for high-scale AI services