prompt-engineerlisted

# 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: | Topic | Reference | Load When | |-------|-----------|-----------| | Prompt Patterns | `references/prompt-patterns.md` | Zero-shot, few-s