prompt-engineering

SKILL.md

Design and optimize prompts for large language models (LLMs) to achieve reliable, high-quality outputs across diverse tasks.

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 mode...

| Goal | Technique | Token Cost | Reliability | Use Case |

使用零樣本、少樣本、思維鍊和結構化輸出技術來設計有效的法學碩士提示。在構建需要可靠輸出的 LLM 應用程序、實施 RAG 系統、創建 AI 代理或優化即時質量和成本時使用。涵蓋 OpenAI、Anthropic 和開源模型以及多語言示例 (Python/TypeScript)。 來源：ancoleman/ai-design-components。

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