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  "createdAt": "2026-07-22",
  "updatedAt": "2026-07-22",
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  "providerLegalNameZh": "杭州烛龙智元科技有限公司",
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    "preparationCount": 1,
    "onlineSupportEntryCount": 1,
    "lessonsWithHandsOn": 20,
    "lessonsWithKnowledgePoints": 20
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          "durationZh": "课前录播",
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            "Transformer自注意力机制",
            "Embedding与语义空间表征",
            "Token分词与处理原理",
            "大模型预训练与监督微调(SFT)",
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          "durationZh": "课前录播",
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            "AI Agent统一能力框架",
            "模型推理性能指标(Latency/Throughput)",
            "多模态与超长上下文窗口技术",
            "国产主流模型技术栈(DeepSeek/Kimi/GLM/Qwen)",
            "AI产品经理模型选型策略"
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            "数据闭环与数据飞轮",
            "AI产品经理六维能力模型",
            "A/B测试与科学实验方法",
            "算法偏见与AI伦理治理",
            "模型优化目标定义"
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          "stageZh": "第3天（线下工作坊）",
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            "生产级RAG五步流程：加载、分块、向量化、检索、生成",
            "高质量知识库构建：语义分块与向量数据库选型",
            "RAG与微调(Fine-tuning)技术选型框架",
            "高级RAG优化：查询转换与重排(Re-ranking)"
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          "titleZh": "RAG全景解析（二）：进阶优化与效果评估",
          "categoryZh": "AI核心技术讲解（共4天）",
          "stageZh": "第3天（线下工作坊）",
          "durationZh": "线下课 3h + 实践 + 答疑",
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            "查询改写与智能路由策略",
            "混合检索与重排序(Re-ranking)",
            "RAGAS效果评估体系",
            "假设性文档嵌入(HyDE)",
            "交叉编码器(Cross-Encoder)精排"
          ],
          "handsOnZh": "设计完整的RAG优化实验方案，包括场景选择、问题定义、技术选型（查询改写、路由、Reranker）和效果评估方法"
        },
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          "titleZh": "Agent原理与多智能体协作",
          "categoryZh": "AI核心技术讲解（共4天）",
          "stageZh": "第3天（线下工作坊）",
          "durationZh": "线下课 3h + 实践 + 答疑",
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            "Agent"
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          "descriptionZh": "深入剖析AI Agent的核心工作原理与四大关键组件，掌握ReAct等主流设计模式，探索CrewAI、AutoGen等前沿多智能体协作框架。",
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            "Agent核心组件：规划、记忆、工具、行动",
            "ReAct推理与行动框架",
            "Plan-and-Execute设计模式",
            "Function Calling工具调用机制",
            "多智能体协作框架(CrewAI/AutoGen)"
          ],
          "handsOnZh": "分组设计一个多Agent协作的产品方案，定义Agent角色分工、通信协议和任务编排流程"
        },
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          "titleZh": "多模态大模型技术与产品设计",
          "categoryZh": "AI核心技术讲解（共4天）",
          "stageZh": "第3天（线下工作坊）",
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            "视觉编码器(ViT)与跨模态对齐",
            "多模态信息融合机制",
            "扩散模型(Diffusion Models)原理",
            "模型蒸馏与量化加速",
            "国产多模态模型(Kimi/通义千问/GLM)对比"
          ],
          "handsOnZh": "设计一个多模态AI产品概念（如AI菜谱生成助手），完成不少于500字的产品设计文档(PRD)初稿"
        },
        {
          "lessonIndex": 5,
          "entryType": "core_lesson",
          "countsAsCoreLesson": true,
          "titleZh": "AI在B/C端行业的落地场景与价值分析",
          "categoryZh": "AI落地场景与动手实战",
          "stageZh": "第4天（线下集中）",
          "durationZh": "线下课 2h + 实践 + 答疑",
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          "descriptionZh": "剖析AI在金融、法律、教育、电商等行业的B/C端落地场景，掌握系统的场景发现框架与ROI价值评估方法。",
          "knowledgePointsZh": [
            "B端AI应用价值框架",
            "C端AI应用价值框架",
            "大模型场景发现三步法",
            "AI项目ROI量化评估模型",
            "金融风控中的非结构化数据分析",
            "基于大模型的智能文档审查"
          ],
          "handsOnZh": "撰写一份\"AI+教育\"领域的迷你商业需求文档(Mini-BRD)，包含项目背景、目标用户、核心功能设计和商业价值评估"
        },
        {
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          "entryType": "guided_review",
          "countsAsCoreLesson": false,
          "titleZh": "RAG+Agent作业点评与学员答疑",
          "categoryZh": "AI落地场景与动手实战",
          "stageZh": "第4天（线下集中）",
          "durationZh": "线下答疑",
          "coreTags": [
            "RAG",
            "Agent"
          ],
          "descriptionZh": "导师逐一点评RAG知识库构建方案和Agent协作设计，深度反馈技术方案的可行性与优化方向。",
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          "handsOnZh": null
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        {
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          "entryType": "core_lesson",
          "countsAsCoreLesson": true,
          "titleZh": "动手实战：OpenClaw部署与应用",
          "categoryZh": "AI Agent平台实战",
          "stageZh": "第5天（线下实操）",
          "durationZh": "线下课 2h + 实践 + 答疑",
          "coreTags": [
            "OpenClaw"
          ],
          "descriptionZh": "深入剖析GitHub十万星标项目OpenClaw，掌握其核心架构与产品策略，通过实战部署构建可自托管、连接多模型的个人AI助手。",
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            "OpenClaw中心辐射式架构",
            "Agent沙箱安全机制",
            "Docker化部署与模型连接",
            "Webhook与IM集成",
            "提示注入(Prompt Injection)攻防"
          ],
          "handsOnZh": "通过Docker实战部署OpenClaw，连接大语言模型，并集成到Slack等IM工具中，构建个人专属AI助手"
        },
        {
          "lessonIndex": 8,
          "entryType": "core_lesson",
          "countsAsCoreLesson": true,
          "titleZh": "动手实战：低代码Agent平台Dify/Coze",
          "categoryZh": "AI Agent平台实战",
          "stageZh": "第5天（线下实操）",
          "durationZh": "线下课 2h + 实践 + 答疑",
          "coreTags": [
            "Agent"
          ],
          "descriptionZh": "深入掌握Dify与Coze两大低代码平台，通过可视化工作流与丰富插件，实战构建智能问答、客服机器人等企业级Agent应用。",
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            "Dify工作流编排引擎",
            "Coze的Bot构建与多渠道发布",
            "RAG知识库可视化构建",
            "Agent插件与工具调用",
            "Human-in-the-Loop人在环路机制",
            "低代码平台技术选型(Dify/Coze/FastGPT)"
          ],
          "handsOnZh": "基于Dify或Coze平台，构建一个智能助教机器人，包括创建知识库、设定角色、实现基础问答与特定工作流"
        },
        {
          "lessonIndex": 9,
          "entryType": "core_lesson",
          "countsAsCoreLesson": true,
          "titleZh": "LangChain核心拆解与AI编程入门",
          "categoryZh": "AI Agent平台实战",
          "stageZh": "第5天（线下实操）",
          "durationZh": "线下课 2h + 实践 + 答疑",
          "coreTags": [],
          "descriptionZh": "深入掌握LangChain核心组件与AI编程新范式，驾驭Cursor等AI原生工具，通过代码级理解力构建复杂大模型应用。",
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            "LangChain核心组件体系",
            "LangChain表达式语言(LCEL)",
            "LangSmith可观测性平台",
            "AI原生IDE(Cursor/Windsurf)",
            "Vibe Coding自然语言编程范式",
            "代码库上下文感知技术"
          ],
          "handsOnZh": "使用Cursor通过自然语言指令辅助编写并运行一个完整的LangChain应用，亲身体验Vibe Coding编程新范式"
        }
      ]
    },
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      "moduleId": 3,
      "titleZh": "模块三：AI产品全流程实战",
      "subtitleZh": "企业级项目 + 方法论",
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        {
          "lessonIndex": 1,
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          "titleZh": "项目一：企业级AIGC内容平台",
          "categoryZh": "AI项目实战训练及拆解（超大型0-1项目）",
          "stageZh": "第6天（线下项目冲刺营）",
          "durationZh": "线下课 4h + 实践 + 答疑",
          "coreTags": [],
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            "RAG知识库构建与管理",
            "多模态内容生成引擎",
            "AI审核与校对工作流",
            "模型效果评估体系设计",
            "金融行业应用场景分析"
          ],
          "handsOnZh": "为\"智能客服问答\"场景设计迷你版PRD，包含背景目标、用户故事、核心功能详设（RAG知识库管理、AI辅助回答）及效果评估指标"
        },
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          "lessonIndex": 2,
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          "countsAsCoreLesson": true,
          "titleZh": "项目二：企业级智能客服机器人",
          "categoryZh": "AI项目实战训练及拆解（超大型0-1项目）",
          "stageZh": "第6天（线下项目冲刺营）",
          "durationZh": "线下课 4h + 实践 + 答疑",
          "coreTags": [],
          "descriptionZh": "从0到1构建企业级智能客服，掌握意图识别、RAG等核心技术，学习AI辅助、自动化评测等创新模式，打造能创造商业价值的AI产品。",
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            "意图识别系统设计",
            "RAG检索增强生成实战",
            "向量数据库与知识库治理",
            "AI辅助与人机协同",
            "数据驱动与A/B测试",
            "智能客服工作流自动化"
          ],
          "handsOnZh": "选择一款主流App，逆向分析其智能客服的需求场景与用户流程，从产品经理视角提出至少3个迭代优化建议"
        },
        {
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          "entryType": "core_lesson",
          "countsAsCoreLesson": true,
          "titleZh": "AI产品经理核心工作方法论",
          "categoryZh": "AI产品经理核心工作方法",
          "stageZh": "第7天（线下集中）",
          "durationZh": "线下课 2h + 实践 + 答疑",
          "coreTags": [],
          "descriptionZh": "深入掌握AI产品从模型选型、效果评估到需求定义的完整方法论，学习构建自动化评测体系与数据驱动决策。",
          "knowledgePointsZh": [
            "模型选型方法论",
            "模型效果评估体系",
            "AI产品PRD撰写规范",
            "A/B实验设计与分析",
            "自动化评测流水线",
            "数据驱动决策框架"
          ],
          "handsOnZh": "为L12/L13的实战项目撰写迷你版AI产品PRD，包含产品概述、AI任务定义、模型I/O规格、核心功能、效果评估指标和数据需求"
        },
        {
          "lessonIndex": 4,
          "entryType": "core_lesson",
          "countsAsCoreLesson": true,
          "titleZh": "AI产品商业化与GTM策略",
          "categoryZh": "AI产品经理核心工作方法",
          "stageZh": "第7天（线下集中）",
          "durationZh": "线下课 2h + 实践 + 答疑",
          "coreTags": [],
          "descriptionZh": "深入剖析AI产品的商业模式、成本结构与GTM策略，掌握从技术到市场价值的完整商业闭环，构建平台化生态思维。",
          "knowledgePointsZh": [
            "AI产品商业模式(SaaS/API/订阅制)",
            "AI产品成本结构(算力/模型/数据)",
            "GTM市场进入策略",
            "AI产品定价策略(基于价值/分层)",
            "MaaS平台化战略",
            "AI产品生态建设与网络效应"
          ],
          "handsOnZh": "撰写一份AI产品的迷你商业计划书(Mini-BP)，涵盖产品概述、商业模式、成本预估及GTM策略"
        },
        {
          "lessonIndex": 5,
          "entryType": "guided_review",
          "countsAsCoreLesson": false,
          "titleZh": "项目定义、架构图、流程图点评",
          "categoryZh": "AI产品经理核心工作方法",
          "stageZh": "第7天（线下集中）",
          "durationZh": "线下答疑",
          "coreTags": [],
          "descriptionZh": "导师逐一点评学员的项目定义文档、系统架构图和业务流程图，并提供产品逻辑与技术可行性的改进建议。",
          "knowledgePointsZh": [],
          "handsOnZh": null
        },
        {
          "lessonIndex": 6,
          "entryType": "core_lesson",
          "countsAsCoreLesson": true,
          "titleZh": "下一代AI协议：MCP与A2A深度解析",
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            "行为面试STAR-L模型",
            "大厂面试全流程拆解"
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          "handsOnZh": null
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  "license": "https://creativecommons.org/licenses/by/4.0/",
  "title": "DragonAI 卓越班 20 节核心课与完整课程安排 v1",
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  "url": "https://course.dragonai.tech/datasets/dragonai-ai-product-manager-course-syllabus-v1.json",
  "creator": "烛龙智元 DragonAI 教研团队"
}
