prompt-engineering-patterns

Prompt Engineering Patterns

Master advanced prompt engineering techniques to maximize LLM performance, reliability, and controllability.

Do not use this skill when

• The task is unrelated to prompt engineering patterns
• You need a different domain or tool outside this scope

Instructions

• Clarify goals, constraints, and required inputs.
• Apply relevant best practices and validate outcomes.
• Provide actionable steps and verification.
• If detailed examples are required, open resources/implementation-playbook.md.

Use this skill when

• 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

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

• Ste