Prompt Engineer

Overview

This skill covers systematic design and iteration of prompts for large language models (LLMs). It applies proven techniques — zero-shot, few-shot learning, chain-of-thought reasoning, role prompting, structured output constraints, system prompt design, and multi-step prompt chaining — to improve accuracy, consistency, and reliability of LLM outputs. The skill is applicable to any LLM API (OpenAI, Anthropic, Gemini, Mistral, open-source models) and covers both single-turn and multi-turn conversation design, as well as production-grade prompt templates with variable injection.

When to Use

• A prompt produces inconsistent, vague, or off-format outputs and needs iteration
• Designing prompts that must return structured JSON, XML, Markdown, or CSV output
• Building few-shot examples to guide classification, extraction, or transformation tasks
• Creating system prompts that establish persona, tone, constraints, or output rules
• Chaining multiple prompts together for complex multi-step reasoning tasks
• Reducing hallucination by adding grounding instructions, citations requirements, or self-checks
• Optimizing a prompt for a specific model (GPT-4, Claude, Llama, etc.) given its strengths
• Converting a vague user request into a precise, production-ready prompt template