Prompt Engineer

When to Use This Skill

• Designing prompts for new LLM applications
• Optimizing existing prompts for better accuracy or efficiency
• Implementing chain-of-thought or few-shot learning
• Creating system prompts with personas and guardrails
• Building structured output schemas (JSON mode, function calling)
• Developing prompt evaluation and testing frameworks
• Debugging inconsistent or poor-quality LLM outputs
• Migrating prompts between different models or providers

Core Workflow

1. Understand requirements — Define task, success criteria, constraints, and edge cases
2. Design initial prompt — Choose pattern (zero-shot, few-shot, CoT), write clear instructions
3. Test and evaluate — Run diverse test cases, measure quality metrics
   • Validation checkpoint: If accuracy < 80% on the test set, identify failure patterns before iterating (e.g., ambiguous instructions, missing examples, edge case gaps)
4. Iterate and optimize — Make one change at a time; refine based on failures, reduce tokens, improve reliability
5. Document and deploy — Version prompts, document behavior, monitor production

Reference Guide

references/prompt-patterns.md

references/prompt-optimization.md

references/evaluation-frameworks.md

references/structured-outputs.md

references/system-prompts.md

Prompt Examples

Zero-shot vs. Few-shot

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Sentiment: Negative

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Sentiment: Neutral

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Before/After Optimization

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Constraints

MUST DO

• Test prompts with diverse, realistic inputs including edge cases
• Measure performance with quantitative metrics (accuracy, consistency)
• Version prompts and track changes systematically
• Document expected behavior and known limitations
• Use few-shot examples that match target distribution
• Validate structured outputs against schemas
• Consider token costs and latency in design
• Test across model versions before production deployment

MUST NOT DO

• Deploy prompts without systematic evaluation on test cases
• Use few-shot examples that contradict instructions
• Ignore model-specific capabilities and limitations
• Skip edge case testing (empty inputs, unusual formats)
• Make multiple changes simultaneously when debugging
• Hardcode sensitive data in prompts or examples
• Assume prompts transfer perfectly between models
• Neglect monitoring for prompt degradation in production

Output Templates

1. Final prompt with clear sections (role, task, constraints, format)
2. Test cases and evaluation results
3. Usage instructions (temperature, max tokens, model version)
4. Performance metrics and comparison with baselines
5. Known limitations and edge cases

Coverage Note