prompt-engineering-patterns

Topics in the source

• Prompt Engineering Patterns
• Do not use this skill when
• Instructions
• Use this skill when
• Core Capabilities
• 1. Few-Shot Learning
• 2. Chain-of-Thought Prompting
• 3. Prompt Optimization

Master advanced prompt engineering techniques to maximize LLM performance, reliability, and controllability. The task is unrelated to prompt engineering patterns You need a different domain or tool outside this scope 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. 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 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 Step-by-step reasoning elicitatio

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