Prompt Engineering Patterns

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This skill empowers developers to master sophisticated prompt engineering patterns for Claude and other LLMs, moving beyond simple instructions to production-grade implementations. It provides structured frameworks for few-shot learning, chain-of-thought reasoning, and dynamic template interpolation, ensuring consistent and high-quality outputs. By integrating specialized system prompts and error-recovery logic, it helps developers build robust AI-driven features that handle edge cases gracefully while optimizing for both token efficiency and response latency.

Características Principales

• Structured Chain-of-Thought (CoT) reasoning elicitation
• Dynamic few-shot learning with semantic example selection
• Modular prompt template systems with variable interpolation
• 0 GitHub stars
• Automated prompt optimization and A/B testing workflows
• System prompt design for specialized AI assistant roles

Casos de Uso

• Optimizing inconsistent LLM outputs for complex reasoning tasks
• Reducing token usage and latency in production AI applications
• Implementing reliable structured data extraction and SQL generation