AI prompt engineering course SEO 分析报告

本报告聚焦关键词AI prompt engineering course，面向翔宇提示工程学院有限公司提供系统化的SEO深度分析，涵盖如下几个部分： 1. 竞争者信息 2. 竞争分析 3. 用户意图分析 4. 机会识别与洞察分析 5. 内容大纲 旨在精准覆盖产品经理、内容创作者、自由职业者 的需求并实现如下目标： 系统化 AI 提示词与生成式 AI 实战在线课程

AI prompt engineering course竞争者分析

本报告针对目标关键词 "AI prompt engineering course" 的搜索引擎竞争格局进行深入分析，旨在揭示当前排名靠前的竞争者在内容结构、关键词策略及用户体验上的共同特征与差异化优势，为优化内容提供数据支持。

1. 竞争者编码列表

为了方便后续报告中的引用和分析，我们为本次研究的竞争者构建了统一的编码标识：

• C1 (Coursera)
• C2 (Udemy)
• C3 (ASU CareerCatalyst)
• C4 (Learn Prompting)
• C5 (Google Cloud)
• C6 (Reddit)
• C7 (Grow with Google)
• C8 (DeepLearning.AI)

2. Meta标题和描述趋势

对竞争者的Meta标题和描述分析揭示了以下趋势：

• 高频关键词: 核心关键词 "Prompt Engineering" 高度集中，常与 "AI"、"Course"、"ChatGPT"、"Generative AI" 结合出现。其他常见词包括 "Learn"、"Online"、"Guide"、"Basics"、"Complete"、"Developers"、"Certificate"。
• 内容定位策略:
  • 课程平台/提供者 (C1, C2, C3, C7, C8): 强调课程本身 ("Course", "Bootcamp", "Essentials", "Short Course")、学习成果 ("What you'll learn", "Skills"), 目标受众 ("for Everyone", "for AI", "for Developers")。
  • 信息/指南型 (C5): 定位为 "Guide", "Overview", 侧重概念解释 ("What is"), 应用 ("Use Cases"), 策略 ("Strategies").
  • 列表/聚合型 (C4): 定位为最佳课程列表 ("Best Online Courses", "Free & Paid", "with Certificates"), 强调选择多样性。
  • 社区/讨论型 (C6): 直接使用帖子标题 ("AI Prompt Engineering Course").
• 转化率优化元素:
  • 价值导向: 强调学习价值 ("Unlock AI's potential", "Level up your use", "Mastering in-demand skills").
  • 结果强调: 突出证书 ("with Certificates", "Earn a certificate"), 实用技能 ("practical coding skills", "automating workflows").
  • 行动召唤 (Call to Action): Meta描述中常包含隐含的CTA，如 "Learn more", "Find prompt examples". 显式CTA如 "Enroll", "Start here" 可能更多出现在页面内容中，但Meta描述通过强调收益来吸引点击。C7 (Grow with Google) 的Meta描述明确提到 "Learn AI prompting with Google" 和 "teaches you how to write effective prompts in 5 easy steps"。C8 (DeepLearning.AI) 强调 "Level up your use of LLMs with prompt engineering best practices."
  • 吸引力表达: 使用 "Complete", "Basics", "Essentials", "Best", "Bootcamp" 等词汇吸引不同需求的用户。C2 (Udemy) 使用 "(2025)" 暗示内容的时效性。

3. 常见大纲部分/主题

分析竞争者页面的H2/H3标题结构，提炼出以下常见大纲部分/主题：

• 课程/内容简介: "Overview" (C3, C4, C5), "About this course" (C8), "What you'll learn" (C1, C2, C8), "Description" (C2).
• 目标受众与前置要求: "Who this course is for" (C2, C3), "Who should join?" (C8), "Recommended experience" (C1).
• 课程模块/内容详情: "Course modules" (C3), "Module breakdown" (C7), 具体模块名称 (e.g., C1 的 "Prompt Engineering for Generative AI", "Prompt Engineering: Techniques and Approaches"), "Curriculum and Topics Covered" (C4 - 用于描述列表中的每个课程).
• 学习成果与技能: "Outcomes" (C3), "Skills you'll gain" (C1), "What you'll learn" (C1, C2, C8), "Practical skills you will develop" (C3).
• 讲师信息: "Instructors" (C1, C2, C3, C8).
• 课程详细信息 (时长, 价格, 格式): "Details to know" (C1), "Duration" (C3), "Online - self-paced" (C3), "What's included" (C1 - 详细列出视频、阅读材料、作业等).
• 证明与证书: "Earn a certificate" (C3, C7), "with Certificates" (C4).
• 应用场景与案例: "Use cases and examples" (C5), "These skills directly apply to these careers" (C3).
• 核心概念与技巧: "What is prompt engineering?" (C4, C5), "Types of prompts" (C5), "Strategies for writing better prompts" (C5), "Prompt format", "Context and examples" (C5).
• 优势与价值: "Benefits of prompt engineering" (C5), "Why is It Important to Learn Prompt Engineering?" (C4), "Why enroll in…?" (C7).
• 用户评价与证明: "Learner reviews" (C1), "Reviews and Testimonials" (C4), "Hear from our graduates" (C7).
• 相关课程/资源: "Related Courses" (C8), "Keep learning with these related programs" (C7), "Related Google Cloud products and services" (C5).
• 常见问题解答 (FAQ): "Frequently asked questions" (C1, C7).
• Call to Action: 通常隐含在章节标题中，如 "Start your AI journey" (C5), "Unlock access…" (C1, C7). C3 有明确的 "Start here" 链接在介绍部分。

整体来看，课程提供者倾向于使用更详细的课程介绍结构，包括模块明细和学习活动。信息/指南型页面（C5）则侧重于概念、类型和策略的系统讲解。列表型页面（C4）为每个被推荐的课程构建了一套标准化的评估框架。

4. 关键词组合模式 (N-gram分析)

N-gram分析突出了关键词在短语层面的共现模式：

• 核心二元组: "prompt engineering" (C1, C4, C5, C6, C8) 是最核心的二元组，在多个竞争者中高频出现，C4 (Learn Prompting) 的提及次数尤其高 (15次)，因为它是列表型页面，反复提及 "Prompt Engineering Courses"。
• 核心三元组: "prompt engineering for" (C1, C4, C8) 是重要模式，常后接目标群体 ("for Everyone", "for AI", "for Developers") 或平台/工具 ("for ChatGPT"). "generative ai prompt" (C1) 和 "ai prompt engineering" (C1) 表明课程与生成式AI的紧密关联。
• 核心四元组: "generative ai prompt engineering" (C1) 和 "ai prompt engineering basics" (C1) 进一步细化了主题和课程级别。 "prompt engineering for developers" (C8) 精准定位了受众。
• 课程属性相关: "hands on activities" (C7), "google prompting essentials" (C7), "google career certificates" (C7), "earn certificate and badge" (C3) 等N-grams 反映了特定课程的特色和卖点，如实践性、品牌关联、证书等。
• 列表型页面模式: C4 的 N-grams 中频繁出现与评估框架相关的短语，如 "overview and target audience", "curriculum and topics covered", "issuer and credibility", "pros and cons", "reviews and testimonials" (均为10次)，这显著区别于其他类型的页面结构。

这些N-grams揭示了用户搜索时可能使用的长尾关键词，以及竞争者在内容中围绕核心主题构建的常见子主题和价值点。

5. 页面结构与用户体验要素

竞争者在页面结构和用户体验要素方面采用了多种策略：

• 内容组织逻辑:
  • 模块化: C1 (Coursera) 将课程内容细致划分为模块，并列出每个模块的学习活动（视频、阅读、作业、应用）。C7 (Grow with Google) 也采用模块化结构。
  • 流程化/步骤化: C7 (Grow with Google) 强调 "5 easy steps"，暗示内容的易学性和结构性。
  • 列表/对比: C4 (Learn Prompting) 采用典型的列表文章结构，为每个推荐课程提供标准化的小节，便于用户快速对比。
  • 问答式: C1 和 C7 的 FAQ 部分，C5 的 "What is…?" 系列H2标题，都采用了问答形式来组织信息，直接回应用户潜在问题。
  • 指南式: C5 (Google Cloud) 的结构是典型的技术指南，从定义、需求、类型到应用和策略，逻辑清晰。
• 功能性元素应用:
  • 列表 (Lists): 广泛使用列表展示学习成果、模块内容、策略步骤、FAQ等，提高信息可读性 (C1, C3, C5, C7)。
  • 多媒体内容 (Multimedia): C1 明确列出包含 "videos"，C7 也提到 "short video segments"，表明视频是重要的内容形式。C4 和 C8 的Meta图片链接暗示使用了图片来增强视觉吸引力。
  • 数据表格 (Data Tables): C4 的 "Course Comparison at a Glance" 强烈暗示使用了表格来对比不同课程的关键信息。
  • 互动表单 (Interactive Forms): C7 的订阅功能 ("Subscribe to discover even more ways to grow") 暗示了表单的应用。C3 的反馈链接 ("Start here") 也提供互动方式。
  • 导航工具: C3 有 "On this page" 导航，便于长页面浏览。
• 内容深度与篇幅:
  • 深度课程页: C1, C2, C8 作为具体的课程页面，内容通常较深，包含详细的课程大纲、讲师信息、学习活动明细等。C1 的模块包含多个H4/H5层级，显示内容粒度较细。
  • 简短课程页: C3 (ASU) 篇幅相对较短，强调快速学习（2小时）和核心产出（证书/徽章）。
  • 指南/文章页: C5 (Google Cloud) 和 C4 (Learn Prompting) 篇幅适中到偏长，前者侧重概念体系，后者侧重信息聚合和对比。
  • 社区页: C6 (Reddit) 页面结构相对简单，核心是帖子内容和用户评论。

6. 竞争者总结与差异化分析

• C1 (Coursera): 作为大型在线教育平台，提供IBM品牌的生成式AI基础课程，内容结构化、模块细致，适合广泛的初学者。强调证书和平台内其他相关课程，旨在平台生态内留存用户。其差异化在于平台品牌、合作方权威性(IBM)和模块化学习体验。
• C2 (Udemy): 提供“完整训练营”式课程，重点在于实践编码技能以及与具体AI工具（GPT-4, Stable Diffusion, GitHub Copilot）的结合。定位可能更偏向希望将提示工程应用于实际开发或内容创作的学员。其差异化在于强调实战和工具应用。
• C3 (ASU CareerCatalyst): 由亚利桑那州立大学提供，课程短小精悍（2小时）、价格固定且较低，快速获得证书/徽章，并明确关联到具体的职业路径（商业分析师、内容策略师）。差异化在于快速入门、职业导向和大学背书。
• C4 (Learn Prompting): 定位为提示工程学习资源的聚合与评测网站，本身不直接提供课程，而是推荐并分析第三方课程。通过对比不同课程的优劣，帮助用户进行选择。差异化在于信息中立性（评测角度）、资源整合和用户决策辅助。
• C5 (Google Cloud): 提供一份关于提示工程的权威性指南，侧重概念解释、理论类型和策略方法。作为Google Cloud的子页面，其目标更侧重于知识普及和引导用户了解其AI产品及服务。差异化在于品牌权威性和基础知识体系梳理。
• C6 (Reddit): 作为一个社区讨论平台，用户通过发帖提问获取他人的经验和推荐。内容非结构化，但具有真实的用户互动和多样的观点。差异化在于用户生成内容、社区互动和非正式信息交流。
• C7 (Grow with Google): Google自家的“提示工程入门”课程，以易学（5步法）、实践性和品牌信用为卖点。课程设计注重用户反馈和学习体验，并与其他Google职业证书项目关联。差异化在于Google官方出品、用户友好设计和与生态系统的联动。
• C8 (DeepLearning.AI): 由Andrew Ng等知名AI专家创立的平台，其课程特别针对开发者，强调提示工程在自动化工作流、链式调用LLMs和构建聊天机器人等技术应用中的最佳实践。差异化在于面向开发者、技术深度和专家背景。

总结来看，AI提示工程课程的竞争者呈现多元化格局，涵盖了大型平台、大学、技术公司、内容聚合站和社区。他们在目标受众（大众、开发者、职业人士）、课程深度（基础、进阶）、形式（课程、指南、列表、讨论）和价值主张（证书、实战、权威、快速、社区）上形成了各自的差异化。要在这个领域脱颖而出，需要清晰定位目标受众，并提供与竞争者不同的独特价值或更优质的学习体验。

AI prompt engineering course用户意图分析

1. 主要用户意图

用户搜索“AI prompt engineering course”的核心意图是寻找关于AI提示工程（Prompt Engineering）的结构化学习资源或培训课程。这主要属于信息获取型 (Informational) 意图，用户旨在深入了解该领域知识并学习相关技能。同时，由于“course”通常代表一种付费或需要投入的教育产品，因此也强烈包含商业调研型 (Commercial Investigation) 意图，用户正在评估不同的课程选项，为未来的潜在购买或注册做准备。这不是一个简单的导航型或直接交易型查询。

2. 次要意图

除了找到课程本身，用户可能还伴随以下次要意图或相关需求：

• 了解提示工程的定义、重要性及应用场景。
• 寻找免费的提示工程入门资源（如文章、教程、指南）。
• 比较不同课程的内容、特色、讲师背景、价格和时长。
• 查看课程评价和用户反馈。
• 了解学习提示工程所需的先决条件。
• 探索提示工程在不同领域（如内容创作、编程、数据分析、艺术设计）的具体应用。
• 了解学习提示工程对职业发展的潜在帮助。
• 寻找课程是否提供证书或认证。
• 寻找课程中的实践案例或练习机会。

3. 隐含用户画像

搜索此关键词的用户画像可能包括：

• 角色：
  • AI模型使用者：内容创作者、营销人员、设计师、教育工作者等，日常工作中依赖大型语言模型或生成式AI工具。
  • 技术专业人士：开发者、数据科学家、研究员，希望更高效地利用AI模型进行开发或研究。
  • 产品经理：负责集成AI功能到产品中，需要理解如何优化用户与AI的交互。
  • 学生或学习者：对AI技术感兴趣，希望掌握未来重要的技能。
  • 寻求职业转型者：看好AI领域的发展前景，希望学习相关技能进入该行业。
• 痛点：
  • 难以通过简单的提示获得满意的AI输出。
  • 不知道如何系统化地提升与AI的沟通效率。
  • 面对复杂的AI任务时，缺乏有效的提示策略。
  • 市场上关于提示工程的信息碎片化，缺乏系统性的学习路径。
  • 希望提升工作效率和产出质量，但受限于AI使用技能。
• 背景： 可能具有一定的技术背景（如编程、数据分析）或非技术背景但对AI工具使用有基础了解。普遍对AI技术的发展及其应用潜力有认知。
• 购买产品与获取服务需求： 需要一个结构完整、内容实用、理论与实践结合的在线或线下课程。课程最好有清晰的学习目标、经验丰富的讲师、丰富的案例和练习，并能提供课程资料、社区支持甚至结课证书。对课程的灵活性（如自定进度）、价格透明度以及可靠性有较高要求。

4. 买家旅程阶段

用户搜索“AI prompt engineering course”的行为处于考虑阶段 (Consideration Stage)。用户已经意识到了有效使用AI工具的重要性，并认识到“提示工程”是解决这一问题的一种方法。他们现在正积极地研究和比较不同的学习方案（即课程），以找到最适合自己的那个。他们尚未做出最终的购买决定，而是在评估各种选择的价值和可行性。

5. 预期服务/内容

为了满足用户意图，目标页面（如课程详情页、课程列表页或相关Landing Page）应逻辑上提供以下特定服务或信息类型：

• 课程核心信息：
  • 课程名称及清晰的价值主张（学习后能获得什么）。
  • 详细的课程介绍和亮点。
  • 完整的课程大纲/目录。
  • 学习目标或学习成果列表。
  • 适合人群/前提条件说明。
  • 课程形式（在线视频、直播、自学、混合等）。
  • 课程时长或预计完成时间。
  • 讲师介绍（经验、背景）。
• 证明与支持：
  • 学员评价或成功案例。
  • 结课证书示例（如果提供）。
  • 课程提供方的背景或资质。
• 购买与支持：
  • 课程价格和支付方式。
  • 退款政策（如果适用）。
  • 常见问题解答 (FAQ)。
  • 联系方式或支持渠道。
• 激发兴趣：
  • 课程演示视频或免费试听章节。
  • 精彩案例或练习示例。
  • 与课程相关的行业趋势或前景分析。
  • 明确的行动号召（如“立即报名”、“免费试听”、“下载课程大纲”）。

6. 问题/解决方案框架

• 应如何阐述用户的核心问题：
  • 强调当前许多人在使用AI工具时遇到的挑战：“你是否也发现，即使使用了最先进的AI模型，仍然难以获得真正精准、符合预期的结果？” “是否觉得与AI沟通像在‘盲盒’抽取，效率低下且效果不稳定？” “你是否知道，掌握提示工程是释放AI强大能力的关键，但却不知从何学起？”
  • 触及痛点：“无效的提示不仅浪费时间，更可能让你错失利用AI提升效率、创造价值的机会。”
• 如何将服务定位为针对此特定查询的解决方案：
  • 将课程定位为解决上述问题的系统性、高效路径：“我们的[课程名称]正是为你设计的解决方案。我们提供一套全面、系统的学习体系，手把手教你掌握与AI高效对话的艺术与科学——提示工程。”
  • 突出课程价值：“通过我们的课程，你将学会如何设计清晰、精确、富有策略性的提示，从而稳定地从AI获得高质量输出，无论是内容、代码还是创意。告别低效尝试，开启AI赋能的高效工作模式。”
  • 强调课程特色与优势：“我们的课程结合理论讲解与大量实战案例，涵盖[课程亮点，如：多种模型应用、不同场景策略、高级技巧等]，确保你学完即可用，真正解决你在AI使用中遇到的实际问题。”

AI prompt engineering course机会识别与洞察分析

本报告基于对目标关键词“AI prompt engineering course”的竞争者分析和用户意图分析，旨在识别当前市场的基石需求、差异化机会、核心SEO要素以及用户体验优化策略，为构建一个具有竞争力的内容与产品方案提供数据驱动的洞察。

1. 内容基石分析

依据对用户意图和竞争者内容的综合分析，一个成功的“AI prompt engineering course”页面必须包含以下构成内容策略基础的“行业标配”元素。这些元素是满足用户基本需求和建立页面权威性的前提，是后续差异化策略的基准。

• 课程核心信息呈现：

  • 课程名称与价值主张： 清晰的课程标题和一句话概括学习后能获得的价值（如：掌握与AI高效沟通的技能）。
  • 详细课程介绍： 对课程背景、目标、解决痛点（如：AI输出不精准、效率低下）进行阐述。
  • 学习目标与成果： 列出具体的、可衡量的学习成果（Skills you'll gain），用户学完后能做什么。
  • 课程大纲/模块列表： 展示课程的整体结构和主要内容单元（Course modules/Curriculum），使用H2/H3层级体现结构性。
  • 适合人群与先决条件： 明确说明课程面向的目标受众（Who this course is for）以及是否有任何前提知识要求（Recommended experience）。
  • 课程形式与时长： 说明是自学、直播、视频等形式，以及预估的学习时间或总时长（Duration）。
  • 讲师信息： 提供讲师的背景、经验和专业性，增强课程可信度。

• 信任与证明要素：

  • 证书/认证信息： 如提供结课证书，明确说明并展示其价值（Earn a certificate）。
  • 用户评价/案例： 展示其他学员的反馈或成功应用案例，提供社会证明（Learner reviews/Testimonials）。

• 商业与支持信息：

  • 价格与支付信息： 清晰列出课程费用和报名方式。
  • 常见问题解答 (FAQ)： 回答用户在报名、学习过程中可能遇到的常见问题，减少疑虑。
  • Call to Action (CTA)： 明确、贯穿始终的引导用户进行下一步操作（如：立即报名、免费试听）。

这些基石要素在Coursera (C1)、Udemy (C2)、ASU (C3)、Grow with Google (C7)、DeepLearning.AI (C8) 等课程提供者页面中普遍存在，并被用户意图分析验证为用户在“商业调研”阶段评估课程时必须获取的关键信息。缺失这些内容将导致页面无法有效满足用户需求，难以在竞争中立足。

2. 内容与体验优化机会

在基石内容之上，通过深入挖掘用户意图的显性和隐性需求，并对比竞争者未能有效满足或忽略的关键领域，我们识别出以下构建独特竞争优势的“价值创新缺口”：

• 机会点 1：深度细分领域的提示工程应用（内容差异化）

  • 洞察： 用户画像显示有多种角色（内容创作者、开发者、数据分析师等），他们的提示工程需求和应用场景差异很大。竞争者普遍提供通用或面向开发者的课程（C8），但针对非技术或特定垂直领域（如市场营销、法律、教育、艺术设计）的提示工程策略及案例覆盖不足。
  • 优化策略： 提供针对特定职业或行业的提示工程高级模块或独立课程。例如，增加“面向内容营销的AI提示工程”、“利用AI进行数据分析的提示工程技巧”等模块。内容应包含大量该领域专属的案例、模板和工具集成指导。这将精准吸引特定用户群体，填补市场空白。

• 机会点 2：强调实战、解决实际痛点（内容与体验优化）

  • 洞察： 用户痛点在于“难以通过简单的提示获得满意输出”、“不知道如何系统化提升沟通效率”。竞争者提到了实践（C7），Udemy (C2) 强调编码，但普遍缺乏对“如何解决我的具体工作问题”的直接回应。用户希望学到的是能立即应用、解决实际挑战的技能，而非纯理论。
  • 优化策略：
    • 内容： 在课程大纲和描述中，将每个技巧或模块与解决的具体问题联系起来。例如，“学习链式提示技巧，解决复杂任务一次性失败的问题”。设计更多贴近用户实际工作场景的案例分析和互动练习。
    • 体验： 提供在线练习环境或可下载的实战模板/工作表。展示课程如何帮助用户“告别低效尝试，开启高效模式”，使用用户痛点框架（如用户意图分析中的问题阐述方式）贯穿页面文案。强调“手把手”、“学完即用”等。

• 机会点 3：构建学习社区与持续支持（体验优化）

  • 洞察： Reddit (C6) 的存在表明用户有交流、提问、获取他人经验的需求。许多在线课程缺乏有效的课后交流和持续支持机制。
  • 优化策略： 建立专属的学员社区（论坛、微信群、Discord等），提供讲师或助教答疑。定期组织线上交流会或分享活动。这将显著提升用户学习体验和满意度，形成口碑传播，构建护城河。

• 机会点 4：透明化与个性化的课程价值展示（体验优化）

  • 洞察： 用户处于“商业调研”阶段，需要比较不同课程。Learn Prompting (C4) 通过列表满足了部分需求，但用户在单一课程页面上仍需大量信息来判断“这是否适合我”。
  • 优化策略：
    • 提供清晰的“课程对比一览”（如果提供不同级别或相关的课程）。
    • 设计交互式元素，帮助用户判断课程适合度（如“你是否遇到以下问题？”，选择后推荐相应课程部分或技巧）。
    • 提供免费试听章节或精简版入门指南（如C5的指南），降低用户决策门槛，建立初步信任。

这些机会点旨在通过提供超越基础内容的功能、更贴近用户实际需求的场景化教学、以及更优质的学习服务体验，来填补竞争者未能完全覆盖的价值缺口，从而吸引并留住目标用户。

3. SEO核心要素优先级

结合竞争者Meta标题、描述、N-gram分析以及用户意图分析，以下是提升内容相关性、权威性及排名的核心SEO要素优先级，它们将指导页面架构与核心内容创作：

• 优先级 1：核心关键词与概念（高频、高相关性）

  • 关键词/概念： Prompt Engineering, AI, Course, Learn, Online, Generative AI, ChatGPT.
  • 语义集群： "AI Prompt Engineering Course", "Learn AI Prompt Engineering Online", "Generative AI Prompt Engineering".
  • 重要性： 用户搜索的核心词，竞争者标题和N-gram中出现频率最高。直接关联用户主意图。
  • 应用位置： 页面Title标签、Meta Description、H1标题、导语/第一段、URL slug（如果可能）、正文多次自然提及（尤其是开篇）。

• 优先级 2：核心服务属性与价值点（高相关性、用户关注）

  • 关键词/概念： Guide, Basics, Essentials, Bootcamp, Certificate, Skills, Mastering, Practical, Hands-on.
  • 语义集群： "AI Prompt Engineering Basics", "Mastering Prompt Engineering Skills", "Earn AI Certificate Online", "Practical Prompt Engineering Guide".
  • 重要性： 描述课程形式、学习成果、价值主张和入门/进阶级别。直接响应用户对“学什么”、“能得到什么”的需求。
  • 应用位置： H2/H3标题（如“提示工程基础”、“掌握核心技巧”、“获得认证”）、课程介绍、学习目标列表、CTA文案、正文相关段落。

• 优先级 3：目标受众与特定应用（长尾、细分）

  • 关键词/概念： for Developers, for Everyone, for AI, Use Cases, Strategies, Techniques.
  • 语义集群： "Prompt Engineering for Developers", "Prompt Engineering Techniques and Strategies", "AI Prompt Engineering Use Cases".
  • 重要性： 精准定位特定用户群体或深入探讨特定方面，有助于捕获长尾搜索流量，并提升页面在特定细分领域的专业性。
  • 应用位置： H2/H3标题（如“面向开发者”、“常见应用场景”、“高级提示技巧”）、课程模块名称、适合人群说明、具体案例分析段落。

• 优先级 4：品牌与平台关联（权威性、信任度）

  • 关键词/概念： Google, IBM, Coursera, Udemy, DeepLearning.AI (如果课程与知名平台/品牌合作)。
  • 语义集群： "Learn Prompting with Google", "[Your Brand Name] AI Prompt Engineering Course".
  • 重要性： 借势知名品牌或平台背书，增强权威性和用户信任度。
  • 应用位置： Title标签（如果合作方允许）、Meta Description、H1标题、课程介绍、讲师介绍、证书信息。

SEO内容优先级策略：

1. 标题与开篇： 必须包含优先级1的核心语义集群，清晰传达页面内容是关于“学习在线AI提示工程课程”。
2. H2/H3结构： 围绕优先级2和优先级3的关键概念和属性构建页面的主要章节（如：课程概述、学习成果、课程大纲、适合人群、应用场景、常见问题）。
3. 正文内容： 自然融入各优先级的关键词和语义集群。特别是在介绍模块、技巧、应用场景时，使用相关长尾词。利用语义相近的词汇丰富内容（如：技巧、方法、策略、秘籍）。
4. 列表与表格： 在列出学习成果、课程大纲、FAQ、案例时，多使用列表结构，并包含相关关键词。如C4暗示的课程对比表格，如果页面介绍的是一个课程，可以对比“我们的课程”与“其他类型资源（如免费博客、简单教程）”的优势，并植入相关关键词。
5. 内部与外部链接： 如果有相关博客文章（如“什么是提示工程”）、其他AI课程、或权威外部资源，建立链接，增强页面相关性和权威性。
6. 图像Alt文本： 优化课程证书、界面截图、讲师照片的alt文本，包含相关关键词。

通过系统性地在关键位置和内容中布局这些优先级的关键词和概念，可以显著提升页面在搜索结果中的可见性、点击率和相关性得分，从而有效占领并巩固“AI prompt engineering course”这一搜索生态位。

4. 用户体验与转化提升策略

基于竞争者在内容呈现、信息架构、导航及CTA等方面的表现分析，并结合用户意图中的痛点和期望，提出以下具体、创新的UX优化策略，旨在提升用户满意度、参与度与转化效果：

• 信息架构与导航优化：

  • 诊断： 课程页面内容可能较长，用户需要快速找到特定信息（如价格、大纲、评价）。C3的“On this page”导航是好实践，但并非所有竞品都有。冗长或缺乏层级的页面结构会导致用户迷失。
  • 策略：
    • 侧边/顶部固定导航： 对于较长的课程详情页，实现一个固定的侧边或顶部导航，列出主要章节（如：课程概览、学习内容、讲师、价格、评价、FAQ），用户点击可快速跳转。
    • 折叠/展开模块： 对于详细的课程大纲，可以考虑默认折叠子模块，用户点击再展开，避免页面初看起来过于拥挤（如C1的细粒度模块）。
    • 面包屑导航： 如果课程页面位于网站层级较深的路径下，提供清晰的面包屑导航，帮助用户理解其当前位置及返回路径。

• 内容呈现与可读性提升：

  • 诊断： 纯文本的信息呈现容易让用户疲劳。关键信息可能不够突出。
  • 策略：
    • 多媒体整合： 大力引入课程介绍视频（放置在首屏附近）、讲师介绍视频、模块内容示例视频（如C1列出videos）。使用信息图、流程图解释抽象概念或提示工作流。
    • 突出关键信息： 利用粗体、列表（如C1, C3, C5, C7广泛使用）、引用框、颜色区分等方式，突出学习成果、课程亮点、价格、优惠信息等核心内容。
    • 案例与示例可视化： 对于提示工程的案例，不仅仅是文字描述，要展示输入（prompt）和输出（output）的对比截图，甚至视频演示，增强说服力和直观性。

• 互动性与参与度增强：

  • 诊断： 大部分课程页面是被动的信息接收。缺乏互动可能降低用户兴趣和停留时间。
  • 策略：
    • 小测验/互动评估： 在页面中加入一个简单的“提示工程技能小测试”或“你适合学提示工程吗？”的互动评估，增加趣味性，并引导用户关注其技能差距（对应用户痛点）。
    • 迷你实战模拟： 提供一个简单的在线文本框，让用户输入一个提示，然后返回一个预设的示例AI回复，让用户初步体验提示的效果（无需连接真实AI，只需模拟）。
    • “下载课程大纲”/“免费章节”门槛： 设置一个表单，用户填写邮箱后可下载完整大纲或获得免费试听章节，既增加互动，又收集潜客信息。

• 转化路径与CTA优化：

  • 诊断： CTA可能不够突出、位置不佳，或文案不具吸引力。用户在“商业调研”阶段，需要被引导而非强迫转化。
  • 策略：
    • 多位置CTA： 在首屏、课程介绍后、学习成果后、讲师介绍后、价格信息旁、FAQ结束后等多个逻辑节点设置醒目的CTA按钮。
    • 悬浮/固定CTA： 在用户向下滚动页面时，底部或侧边出现一个固定的“立即报名”或“免费试听”CTA，确保用户在产生意愿时能随时点击。
    • 差异化CTA文案： 根据页面不同部分的侧重点，CTA文案可以有所区别，例如在介绍视频旁是“观看完整课程介绍”，在学习成果旁是“立即掌握这些技能”，在价格旁是“选择你的课程计划”。
    • 强调稀缺性/紧迫性（如果适用）： 如“限时优惠”、“名额有限”等，合理使用可促进转化。
    • 弱化承诺，降低风险： 如果有退款政策或免费试听，在CTA附近强调，降低用户的决策风险（如“不满意？无风险退款！”）。

• 信任构建与社会证明增强：

  • 诊断： 用户在选择课程时高度依赖信任和口碑（如C1, C4, C7的评价）。
  • 策略：
    • 突出讲师权威性： 提供详细且引人入胜的讲师介绍，包括其在AI或提示工程领域的实际项目经验、研究成果或知名度。
    • 精选高质量评价： 不仅仅是数量，展示有代表性的、详细的、突出学习成果或解决痛点的学员评价。可以考虑视频评价或图文并茂的案例。
    • 展示合作方/机构徽标： 如果课程有任何知名机构、大学、或公司背书（如C1与IBM，C3与ASU，C7与Google，C8与DeepLearning.AI），务必在页面醒目位置展示其徽标，借势提升信任度。

通过实施上述UX优化策略，目标页面不仅能提供用户期望的基础信息，更能通过引人入胜的呈现方式、流畅的信息获取流程、增强的互动体验和充分的信任证明，有效引导用户在“商业调研”阶段做出选择，并最终转化为实际报名。这将构建一个超越竞争者、以用户为中心的高效转化漏斗。

内容大纲

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      "description": "本课程是翔宇提示工程学院精心打造的系统化、实战导向的在线课程，旨在帮助学员全面掌握与生成式AI（如ChatGPT）高效沟通的技巧，解决AI输出不精准、效率低下等痛点，最终达成精确、高效的AI协作，释放AI的全部潜力。",
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  "..H3: RECOMMENDED COURSES",
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  "...H4: Live Courses",
  "H1: 10 Best Online Prompt Engineering Courses [Free & Paid] with Certificates",
  ".H2: What is Prompt Engineering?",
  ".H2: Why is It Important to Learn Prompt Engineering?",
  ".H2: Course Comparison at a Glance",
  ".H2: 1. ChatGPT for Everyone by Learn Prompting and OpenAI",
  "..H3: Overview and Target Audience",
  "..H3: Curriculum and Topics Covered",
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  "..H3: Overview and Target Audience",
  "..H3: Curriculum and Topics Covered",
  "..H3: Issuer and Credibility",
  "..H3: Pros and Cons",
  "..H3: Reviews and Testimonials",
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  "..H3: Overview and Target Audience",
  "..H3: Curriculum and Topics Covered",
  "..H3: Issuer and Credibility",
  "..H3: Pros and Cons",
  "..H3: Reviews and Testimonials",
  ".H2: 4. Generative AI: Prompt Engineering Basics by IBM (Coursera)",
  "..H3: Overview and Target Audience",
  "..H3: Curriculum and Topics Covered",
  "..H3: Issuer and Credibility",
  "..H3: Pros and Cons",
  "..H3: Reviews and Testimonials",
  ".H2: 5. Prompt Engineering by Arizona State University",
  "..H3: Overview and Target Audience",
  "..H3: Curriculum and Topics Covered",
  "..H3: Issuer and Credibility",
  "..H3: Pros and Cons",
  "..H3: Reviews and Testimonials",
  ".H2: 6. Introduction to ChatGPT by DataCamp",
  "..H3: Overview and Target Audience",
  "..H3: Curriculum and Topics Covered",
  "..H3: Issuer and Credibility",
  "..H3: Pros and Cons",
  "..H3: Reviews and Testimonials",
  ".H2: 7. DavidsonX: Prompt Engineering for ChatGPT (edX)",
  "..H3: Overview and Target Audience",
  "..H3: Curriculum and Topics Covered",
  "..H3: Issuer and Credibility",
  "..H3: Pros and Cons",
  "..H3: Reviews and Testimonials",
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  "..H3: Overview and Target Audience",
  "..H3: Curriculum and Topics Covered",
  "..H3: Issuer and Credibility",
  "..H3: Pros and Cons",
  "..H3: Reviews and Testimonials",
  ".H2: 9. The Complete Prompt Engineering for AI Bootcamp (2025) by Udemy",
  "..H3: Overview and Target Audience",
  "..H3: Curriculum and Topics Covered",
  "..H3: Issuer and Credibility",
  "..H3: Pros and Cons",
  "..H3: Reviews and Testimonials",
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  "..H3: Overview and Target Audience",
  "..H3: Curriculum and Topics Covered",
  "..H3: Issuer and Credibility",
  "..H3: Pros and Cons",
  "..H3: Reviews and Testimonials",
  ".H2: Bonus: Two Free Courses without Certificates",
  "..H3: 1. Essentials of Prompt Engineering by Amazon Web Services (Coursera)",
  "..H3: 2. ChatGPT Prompt Engineering for Developers by DeepLearning.AI (Coursera)",
  ".H2: Conclusion",
  "..H3: Valeriia Kuka",
  "H1: Further Reading",
  "..H3: Is Prompt Engineering Dead?",
  "..H3: Top AI Risk Management Courses and Certifications for 2025",
  "..H3: Best Certified Ethical Hacker Courses in 2025",
  "..H3: Explore Courses",
  "..H3: Resources",
  "..H3: Follow Us"
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  "..H3: Prompt format",
  "..H3: Context and examples",
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  "..H3: 1. Set Clear Goals and Objectives:",
  "..H3: 2. Provide Context and Background Information:",
  "..H3: 3. Use Few-Shot Prompting:",
  "..H3: 4. Be Specific:",
  "..H3: 5. Iterate and Experiment:",
  "..H3: 6. Leverage Chain of Thought Prompting:",
  ".H2: Benefits of prompt engineering",
  "..H3: Improved model performance",
  "..H3: Reduced bias and harmful responses",
  "..H3: Increased control and predictability",
  "..H3: Enhanced user experience",
  "..H3: Start your AI journey with Google Cloud",
  ".H2: Related Google Cloud products and services",
  "...H4: Additional learning resources to get started",
  "...H4: Take the next step",
  "....H5: Need help getting started?",
  "....H5: Work with a trusted partner",
  "....H5: Continue browsing"
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  "H1: More posts you may like",
  "..H3: Is there any AI -image to text- to make prompts? - It would be interesting",
  "..H3: [Article] Prompt Engineering with ChatGPT: A Guide for Academic Writers",
  "..H3: I think I am going to move back to coding without AI",
  "..H3: Any prompt engineers here? What's your daily work like?",
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  "..H3: Best AI Course That Covers Everything (Including Projects)?",
  "..H3: Noob Question: Are Prompts Inflated with Random Terms?",
  "..H3: Paid courses for excelling data engineering skills",
  "..H3: People who believe AI will replace programmers misunderstand how software development works",
  "..H3: [D] AI infra vs prompt engineering",
  "..H3: Are there any AI courses/programs you've taken to supplement your knowledge?",
  "..H3: In the Age of AI, What Should We Teach Student Programmers?",
  "..H3: Is Graphics Programming still a viable career path in the AI era?",
  "..H3: Mechanical Engineer Coding Journey",
  "..H3: My take on AI projects",
  "..H3: Artificial intelligence in education: Curriculum, special needs, etc. Are we already at a point where we could drastically improve education?",
  "..H3: Best resources to learn more about AI for experienced engineers",
  "..H3: Best Software Engineering courses?",
  "..H3: Prompt Engineering",
  "..H3: Prompt engineering",
  "..H3: Suggest learning path to become AI Engineer",
  "..H3: AI University????",
  "..H3: How much AI are you guys using in your sideprojects?",
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  "..H3: Prompt engineering is not engineering.",
  ".....H6: Related discussions",
  ".H2: Top Posts",
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  "..H3: How are Google Career Essentials different from Google Career Certificates?",
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  "...H4: Thanks for subscribing to the Grow with Google newsletter!",
  "..H3: Subscribe to discover even more ways to grow",
  "...H4: Thanks for subscribing to the Grow with Google newsletter!",
  "..H3: Footer links",
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</competitor8>