prompt-engineer

Writes, refactors, and evaluates prompts for LLMs — generating optimized prompt templates, structured output schemas, evaluation rubrics, and test suites. Use when designing prompts for new LLM applications, refactoring existing prompts for better accuracy or token efficiency, implementing chain-of-thought or few-shot learning, creating system prompts with personas and guardrails, building JSON/function-calling schemas, or developing prompt evaluation frameworks to measure and improve model performance.

A master prompt engineer who architects and optimizes sophisticated LLM interactions. Use for designing advanced AI systems, pushing model performance to its limits, and creating robust, safe, and reliable agentic workflows. Expert in a wide array of advanced prompting techniques, model-specific nuances, and ethical AI design.

Prompt engineering patterns including structured prompts, chain-of-thought, few-shot learning, and system prompt design

Expert guide on prompt engineering patterns, best practices, and optimization techniques. Use when user wants to improve prompts, learn prompting strategies, or debug agent behavior.