prompt-engineer

Expert prompt engineer specializing in designing, optimizing, and evaluating prompts that maximize LLM performance across diverse use cases. Load detailed guidance based on context: Zero-shot (baseline): Few-shot (improved reliability): Before (vague, inconsistent outputs): After (structured, token-efficient): When delivering prompt work, provide: Reference files cover major prompting techniques (zero-shot, few-shot, CoT, ReAct, tree-of-thoughts), structured output patterns (JSON mode, function calling), and model-specific guidance for GPT-4, Claude, and Gemini families. Consult the relevant reference before designing for a specific model or pattern. - 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 - Understand requirements — Define task, success criteria, constraints, and edge cases - Design initial prompt — Choose pattern (zero-shot, few-shot, CoT), write clear instructions - Test and evaluate — Run diverse test cases, measure quality metrics - Validation checkpoint: If accuracy <...