用麦肯锡式结构化方法，帮助对某行业或职业一无所知的新人在短时间内建立入门知识体系。它以多专家协作视角，依次提取15个高频关键词、归纳应用场景分类、给出二级分类与学习书籍及优先级，最终形成可执行的学习路线图。

中文版提示词

请考虑以下情况：
你目前处于需要快速、系统地学习【[请在此处明确填写你感兴趣的行业或职业，例如：人工智能开发工程师]】领域核心知识的场景，对该领域一无所知，需要一份结构化的学习路线图，包括关键概念、分类以及权威学习资料。
示例参考（可选）：提供1-3个优秀的"输入->输出"示例作为风格和格式参考。
为确保高效精确，请以多专家协作视角进行内部思考：
- 行业知识策展人：精准识别【行业/职业】的核心概念、高频术语及其重要性，确保关键词权威实用。
- 学习路径规划师：根据学习者起始点（新人）与目标（入门），设计逻辑清晰的学习路径，包括知识分类与学习优先级。
- 教育资源分析师：检索真实、权威且适合新人的相关书籍与作者，评估其在学习路径中的价值。
- 协作模式：先由行业知识策展人提出关键词列表；随后学习路径规划师与教育资源分析师并行工作，前者进行关键词分类和二级分类，后者根据分类结果搜索并评估书籍，最后由学习路径规划师整合所有信息。
核心目标：系统化构建【你填写的行业/职业】的入门知识体系、高频关键词解析、应用场景分类及推荐学习资源（书籍），形成可执行的学习路线图。
风格与语调：专业、结构化、简洁明了、逻辑严谨，同时对初学者友好；语调权威、指导、鼓励。
目标读者：对【你填写的行业/职业】完全陌生的初学者。
内部思考与执行流程：
1. 知识检索与关联确认：检索并确认关于【行业/职业】的最新核心定义与发展趋势；确保方案紧密关联"新人"学习环境。
2. 核心逻辑深入解构：识别任务本质——通过关键词、分类和书籍推荐系统降低新人入门门槛；重点关注关键词准确性、分类合理性、书籍真实性与实用性。
3. 分步执行策略：
- 步骤1 高频关键词提取与排序：识别该领域15个最高频、对新人最重要的关键词，优先概念性、基础性、应用广泛性词汇，按重要程度降序排列。输出结构：序号（数字）、关键词（中文）、介绍（限20字内）。
- 步骤2 关键词应用场景归纳分类：将15个关键词按其实际应用或功能进行逻辑分组，每个关键词归入一个最恰当的应用场景，分类具有代表性且互斥性尽量高。输出结构：序号、应用场景：所包含的关键词。
- 步骤3 二级分类、学习资源与优先级：将步骤2分类的关键词进行二级分类，从初学者视角给出学习书籍和优先级（高/低），书籍须真实存在且为口碑良好的入门读物，不超过3本并附作者姓名。输出结构（Markdown表格）：应用场景、二级分类、关键词（中文）、优先级、相关书籍（作者）。
4. 自我评估与用户视角检查：设想用户看到内容是否会因缺少关键信息或格式不符而困惑，据此补充或修正可能导致理解障碍的细节。
5. 最终质量自检：检查输出是否严格符合各步骤结构、无冗余说明；关键词数量是否为15个、介绍是否在20字内；书籍是否真实存在、是否附作者、是否不超过3本；优先级是否明确标记为高/低；排除与【行业/职业】无关的内容，避免臆造或过度发散。
最终输出要求：严格按上述分步执行策略顺序逐一呈现各阶段结果，内容直接输出、清晰简洁，完全符合各步骤指定格式（包括Markdown表格结构），不包含任何额外解释或前置说明。

英文版提示词

Consider the following situation:
You are in a scenario where you need to quickly and systematically learn the core knowledge of the [please fill in the industry or profession you are interested in, e.g. AI development engineer] field. You know nothing about it and need a structured learning roadmap including key concepts, classification, and authoritative learning resources.
Example reference (optional): provide 1-3 excellent "input -> output" examples as references for expected style and format.
To ensure efficiency and precision, think with a multi-expert collaboration perspective:
- Industry Knowledge Curator: precisely identify the core concepts, high-frequency terms, and their importance for [industry/profession], ensuring authoritative and practical keywords.
- Learning Path Planner: design a logical learning path based on the learner's starting point (beginner) and goal (entry level), including knowledge classification and learning priority.
- Educational Resource Analyst: search for real, authoritative, beginner-friendly books and authors, and assess their value in the learning path.
- Collaboration mode: the Industry Knowledge Curator first proposes a keyword list; then the Learning Path Planner and Educational Resource Analyst work in parallel — the former classifies keywords into categories and subcategories, the latter searches and evaluates books based on the categories; finally the Learning Path Planner integrates all information.
Core goal: systematically build an entry-level knowledge system, high-frequency keyword explanations, application-scenario classification, and recommended learning resources (books) for [the industry/profession you filled in], forming an executable learning roadmap.
Style and tone: professional, structured, clear, logically rigorous, yet beginner-friendly; authoritative, guiding, encouraging.
Target readers: complete beginners unfamiliar with [the industry/profession you filled in].
Internal thinking and execution process:
1. Knowledge retrieval and confirmation: search and confirm the latest core definitions and development trends of [industry/profession]; ensure all plans closely fit the "beginner" learning environment.
2. Core logic deconstruction: identify the essence of the task — systematically lowering the entry barrier through keywords, classification, and book recommendations; focus on keyword accuracy, reasonable classification, and the authenticity and practicality of recommended books.
3. Step-by-step execution strategy:
- Step 1: extract and rank high-frequency keywords — identify the 15 highest-frequency keywords most important to beginners in the field, prioritizing conceptual, foundational, and broadly applicable terms, in descending order of importance. Output structure: number, keyword (Chinese), introduction (within 20 characters).
- Step 2: categorize keyword application scenarios — group the 15 keywords by their actual application or function, placing each into the most appropriate scenario; categories should be representative and as mutually exclusive as possible. Output structure: number, application scenario: the included keywords.
- Step 3: subcategories, learning resources, and priority — subcategorize the keywords from Step 2 and, from a beginner's perspective, give learning books and priority (high/low); books must be real, well-regarded introductory reads, no more than 3, with author names. Output structure (Markdown table): application scenario, subcategory, keyword (Chinese), priority, related book (author).
4. Self-assessment and user-perspective check: imagine whether a user would be confused by missing key information or a format mismatch, and supplement or correct any details that might cause misunderstanding.
5. Final quality self-check: verify the output strictly matches each step's structure with no redundant explanation; the keyword count is exactly 15 and introductions are within 20 characters; books are real, include authors, and number no more than 3; priorities are marked high/low; exclude anything unrelated to [industry/profession] and avoid fabricating information or over-diverging.
Final output requirements: present each stage's results strictly in the order of the step-by-step strategy above; output directly, clearly, and concisely, fully matching each step's specified format (including Markdown tables), with no extra explanation or preamble.