prompt-engineeringlisted

# Prompt Engineering Design and optimize prompts for large language models (LLMs) to achieve reliable, high-quality outputs across diverse tasks. ## Purpose This skill provides systematic techniques for crafting prompts that consistently elicit desired behaviors from LLMs. Rather than trial-and-error prompt iteration, apply proven patterns (zero-shot, few-shot, chain-of-thought, structured outputs) to improve accuracy, reduce costs, and build production-ready LLM applications. Covers multi-model deployment (OpenAI GPT, Anthropic Claude, Google Gemini, open-source models) with Python and TypeScript examples. ## When to Use This Skill **Trigger this skill when:** - Building LLM-powered applications requiring consistent outputs - Model outputs are unreliable, inconsistent, or hallucinating - Need structured data (JSON) from natural language inputs - Implementing multi-step reasoning tasks (math, logic, analysis) - Creating AI agents that use tools and external APIs - Optimizing prompt costs or latency in production systems - Migrating prompts across different model providers - Establishing prompt versioning and testing workflows **Common requests:** - "How do I make Claude/GPT follow instructions reliably?" - "My JSON parsing keeps failing - how to get valid outputs?" - "Need to build a RAG system for question-answering" - "How to reduce hallucination in model responses?" - "What's the best way to implement multi-step workflows?" ## Quick Start **Zero-Shot Prompt (Python