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 |

使用零样本、少样本、思维链和结构化输出技术来设计有效的法学硕士提示。在构建需要可靠输出的 LLM 应用程序、实施 RAG 系统、创建 AI 代理或优化即时质量和成本时使用。涵盖 OpenAI、Anthropic 和开源模型以及多语言示例 (Python/TypeScript)。 来源：ancoleman/ai-design-components。

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