Prompt Engineering

Systematic techniques for designing, testing, and optimizing prompts that drive LLM-powered applications. Covers prompt anatomy, reasoning strategies, output formatting, evaluation methods, and production prompt management with real examples across classification, extraction, generation, and code tasks.

When to Use This Skill

Use this skill for:

• Designing system prompts for LLM applications
• Implementing chain-of-thought or structured reasoning
• Building few-shot learning examples
• Formatting LLM outputs as JSON, XML, or structured data
• Creating prompt templates with variable injection
• Evaluating and scoring prompt quality
• Managing prompt versions in production
• Reducing token usage and API cost

Trigger phrases: "prompt design", "system prompt", "chain-of-thought", "few-shot", "prompt template", "structured output", "prompt evaluation", "LLM-as-judge", "prompt optimization", "token reduction", "output formatting", "JSON mode"