Prompt Engineering Patterns

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The Prompt Engineering Patterns skill provides a comprehensive toolkit for developers building production-grade LLM applications. It offers sophisticated strategies such as few-shot learning with dynamic example selection, chain-of-thought reasoning traces, and modular template interpolation to ensure AI outputs are consistent, accurate, and cost-effective. By integrating systematic optimization workflows and instruction hierarchies, this skill helps developers move beyond basic instructions to create robust AI-driven features that handle complex logic and edge cases with high reliability.

Características Principales

• Systematic prompt optimization and A/B testing performance metrics
• Structured reasoning patterns including chain-of-thought and self-consistency
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• Advanced few-shot learning with semantic similarity and diversity sampling
• Built-in error recovery patterns and self-verification logic
• Modular prompt template systems with variable interpolation and conditional logic

Casos de Uso

• Developing high-reliability system prompts for specialized production AI agents
• Optimizing token efficiency and reducing latency in high-volume LLM applications
• Improving reasoning accuracy for complex technical tasks like SQL generation