prompt-engineering-patternslisted

# Prompt Engineering Patterns Master advanced prompt engineering techniques to maximize LLM performance, reliability, and controllability. ## When to Use This Skill - Designing complex prompts for production LLM applications - Optimizing prompt performance and consistency - Implementing structured reasoning patterns (chain-of-thought, tree-of-thought) - Building few-shot learning systems with dynamic example selection - Creating reusable prompt templates with variable interpolation - Debugging and refining prompts that produce inconsistent outputs - Implementing system prompts for specialized AI assistants - Using structured outputs (JSON mode) for reliable parsing ## Core Capabilities ### 1. Few-Shot Learning - Example selection strategies (semantic similarity, diversity sampling) - Balancing example count with context window constraints - Constructing effective demonstrations with input-output pairs - Dynamic example retrieval from knowledge bases - Handling edge cases through strategic example selection ### 2. Chain-of-Thought Prompting - Step-by-step reasoning elicitation - Zero-shot CoT with "Let's think step by step" - Few-shot CoT with reasoning traces - Self-consistency techniques (sampling multiple reasoning paths) - Verification and validation steps ### 3. Structured Outputs - JSON mode for reliable parsing - Pydantic schema enforcement - Type-safe response handling - Error handling for malformed outputs ### 4. Prompt Optimization - Iterative refinement