Prompt Engineering

Craft, test, and iterate prompts that deliver reliable outputs across LLMs. Covers prompt optimization techniques, structured prompt design, synthetic test data generation, and evaluation methodology.

When to Use This Skill

• Building or optimizing prompts for AI-powered features
• Crafting system prompts for agents or assistants
• Improving reliability and consistency of LLM outputs
• Generating synthetic test data to validate prompt behavior
• Evaluating prompt performance across edge cases
• Designing prompt chains and pipelines

Quick Reference

Workflow

1. Research: Gather the use case, constraints, and evaluation criteria. Audit existing prompts and model behaviors.
2. Design: Draft structured prompts with examples, constraints, and evaluation hooks. Plan experiments and measurement strategy.
3. Generate test data: Analyze prompt variables, generate diverse and realistic test cases to validate the prompt.
4. Validate: Run prompt trials, capture outputs, document adjustments. Iterate until quality thresholds are met.
5. Deliver: Hand off the final prompt with usage guidance and evaluation results.

Core Principle

When creating prompts, always display the complete prompt text in a clearly marked section. Never describe a prompt without showing it. The prompt must be copyable and self-contained.

Deliverables Checklist

For every prompt engineering task, produce:

• The complete prompt text (displayed in full, properly formatted)
• Explanation of design choices and techniques used
• Usage guidelines (model, temperature, parameters)
• Example expected outputs
• Test cases covering happy path, edge cases, and adversarial inputs

Example Interactions

• "Optimize this system prompt for our code review agent"
• "Create a prompt for extracting structured data from support tickets"
• "Generate test cases to validate this classification prompt"
• "Design a prompt chain for multi-step document analysis"
• "Improve consistency of this summarization prompt"