prompt-engineer
Design, optimize, and evaluate LLM prompts for maximum accuracy and efficiency.
- Covers prompt patterns including zero-shot, few-shot, chain-of-thought, and ReAct, with before/after optimization examples
- Provides structured workflow from requirements definition through testing, iteration, and production deployment with validation checkpoints
- Includes evaluation frameworks, metrics, and test suite generation to measure and improve model performance
- Supports structured output design (JSON mode, function calling), system prompts with personas and guardrails, and cross-model prompt migration
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
- Understand requirements — Define task, success criteria, constraints, and edge cases
- Design initial prompt — Choose pattern (zero-shot, few-shot, CoT), write clear instructions
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