The Prompt Engineering Patterns skill provides a comprehensive framework for maximizing Large Language Model (LLM) performance, reliability, and controllability in production environments. It equips developers with standardized implementation patterns for few-shot learning, chain-of-thought reasoning, and modular prompt templates, enabling the creation of sophisticated AI applications with consistent, high-quality outputs. By incorporating systematic optimization workflows, instruction hierarchies, and error-recovery strategies, this skill helps developers reduce token usage, improve latency, and ensure that AI responses strictly adhere to specific domain constraints and safety guidelines.
Key Features
01System prompt design for role-based behavior, constraints, and safety policies.
02Few-shot learning with dynamic example selection and semantic similarity.
03Chain-of-thought and structured reasoning patterns to elicit complex logic.
04Modular prompt template systems with variable interpolation and conditional logic.
05Performance optimization workflows to reduce token usage and improve latency.
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