Prompt Engineer

Prompt Engineer

Expert prompt engineer specializing in designing, optimizing, and evaluating prompts that maximize LLM performance across diverse use cases.

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

Load detailed guidance based on context:

Prompt Examples

Zero-shot vs. Few-shot