The RO-SCIRAW framework is an innovative prompt methodology created by Kirk Lin, providing a new paradigm for constructing highly precise and efficient…
RO-SCIRAW is an advanced prompt engineering framework pioneered by Kirk Lin, designed to optimize interactions with large language models (LLMs). The framework consists of 8 core components: 1. R - Role: Defines the AI's professional identity and operational mode. \[Required] 2. O - Objectives: Clearly states the specific tasks the AI is expected to accomplish. \[Required] 3. S - Style: Specifies the expression style and tone of the output. \[Optional] 4. C - Content: Provides necessary background information and knowledge base. \[Required] 5. I - Input: Details the data types and formats provided to the AI. \[Required] 6. R - Response: Specifies the desired output format and structure. \[Required] 7. A - Audience: Clarifies the target audience to enhance relevance. \[Optional] 8. W - Workflow: Offers systematic step-by-step guidance. \[Optional] Additionally, it is highly recommended to include an example to help the AI understand the expected output. Your task: Given a topic or task, generate a complete RO-SCIRAW prompt, including all required components (Role, Objectives, Content, Input, Response) and optional components (Style, Audience, Workflow). Also provide a relevant example. Ensure the prompt is clear, structured, and leverages the advantages of the RO-SCIRAW framework.
RO-SCIRAW 是由 Kirk Lin 开创的先进提示词工程框架,用于优化与大型语言模型 (LLMs) 的交互。该框架由 8 个核心组件构成: 1. R - Role (角色): 定义 AI 的专业身份和操作模式。\[必选] 2. O - Objectives (目标): 明确指出期望 AI 完成的具体任务。\[必选] 3. S - Style (风格): 规定输出的表达方式和语气。\[可选] 4. C - Content (内容 / 上下文): 提供必要的背景信息和知识基础。\[必选] 5. I - Input (输入): 详细说明提供给 AI 的数据类型和格式。\[必选] 6. R - Response (响应): 规定期望的输出形式和结构。\[必选] 7. A - Audience (受众): 明确目标群体,以优化输出的相关性。\[可选] 8. W - Workflow (工作流): 提供系统化的步骤指南。\[可选] 此外,强烈建议提供一个示例来帮助 AI 理解预期输出。 你的任务是:给定一个主题或任务,生成一个完整的 RO-SCIRAW 框架提示词。包括所有必选组件 (Role, Objectives, Content, Input, Response) 和可选组件 (Style, Audience, Workflow)。同时提供一个相关的示例。确保提示词清晰、结构化,并能充分利用 RO-SCIRAW 框架的优势。