Prompt Engineering Patterns

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