{ "datasetId": "ai-product-manager-eight-week-execution-roadmap-v1", "schemaVersion": "1.0.0", "createdAt": "2026-07-21", "language": "zh-CN", "license": "https://creativecommons.org/licenses/by/4.0/", "licenseScope": "The eight-week practice sequence, field structure, Chinese descriptions, and fictional project setup created by DragonAI. Third-party source material is excluded and remains under its original terms.", "purposeZh": "把一次 AI 产品练习拆成八个有依赖关系的周次，并逐周记录输入、最小产物、验收门禁、失败回退和传给下一阶段的证据。", "disclosureZh": "DragonAI 运营 AI 产品经理课程。本数据集是课程提供方发布的第一方学习与复盘框架，不是独立认证或学习、面试与就业结果保证。", "boundaryZh": "八周只表示一轮练习的依赖顺序，不是统一学制、职业认证、招聘标准或学习与就业结果保证。学习者可以延长周期，但不应删除验收与回退步骤。", "companionDataset": { "datasetId": "ai-product-manager-readiness-matrix-v1", "url": "https://course.dragonai.tech/datasets/ai-product-manager-readiness-matrix-v1.json", "relationshipZh": "能力矩阵定义八个维度的最低证据；本数据集定义这些证据在一次连续项目中的执行顺序。" }, "sources": [ { "evidenceId": "google-ml-problem-framing-20260720", "title": "Machine Learning Problem Framing: Overview", "publisher": "Google for Developers", "url": "https://developers.google.com/machine-learning/problem-framing/problem-framing" }, { "evidenceId": "google-pair-mental-models-20260720", "title": "People + AI Guidebook: Mental Models", "publisher": "Google PAIR", "url": "https://pair.withgoogle.com/guidebook-v2/chapter/mental-models/" }, { "evidenceId": "google-pair-data-collection-evaluation-20260720", "title": "People + AI Guidebook: Data Collection + Evaluation", "publisher": "Google PAIR", "url": "https://pair.withgoogle.com/guidebook-v2/chapter/data-collection/" }, { "evidenceId": "nist-ai-rmf-core-20260720", "title": "AI Risk Management Framework Core", "publisher": "National Institute of Standards and Technology", "url": "https://airc.nist.gov/airmf-resources/airmf/5-sec-core/" }, { "evidenceId": "google-responsible-ai-introduction-20260720", "title": "Introduction to Responsible AI", "publisher": "Google for Developers", "url": "https://developers.google.com/machine-learning/guides/intro-responsible-ai" } ], "projectSetup": { "ruleZh": "八周使用同一个足够窄的用户任务。没有真实业务项目时可以使用虚构案例，但必须标注假设，不能把模拟结果写成真实用户证据。", "requiredBeforeWeek1Zh": [ "目标用户与当前任务", "当前人工、规则或软件流程", "可用数据与明确排除范围", "一名负责检查可重现性的复核者", "最晚停止继续投入的日期或条件" ], "fictionalExampleZh": "设计一个引用内部制度回答员工问题的知识助手；示例不包含真实用户、企业流程或性能结果。" }, "continuousPracticesZh": [ "第1周建立初始评测任务、成功与停止条件、风险与责任假设，此后每周随证据更新。", "每周至少保存一次产物版本、一次验收运行、失败样例、修改依据、复核人和日期。", "后一周必须消费前一周产物；验收不通过时先回退修正，不用新增功能掩盖证据缺口。" ], "weeks": [ { "week": 1, "phaseId": "problem-framing", "nameZh": "问题定义与基线", "primaryQuestionZh": "这个用户任务为什么需要 AI？", "inputsZh": [ "目标用户与当前任务", "当前流程", "失败成本与排除范围" ], "minimumArtifactsZh": [ "问题卡", "非 AI 基线", "成功条件", "停止条件", "初始评测与风险假设" ], "acceptanceGateZh": "能用同一任务和同一组指标比较 AI、规则或人工方案，并说明选择或不选择 AI 的依据。", "failureReturnZh": "缩小任务范围，补充当前流程、失败成本或可测量结果后重新比较。", "handoffOutputsZh": [ "问题卡", "基线记录", "初始评测任务", "风险与责任假设" ], "evidenceIds": [ "google-ml-problem-framing-20260720" ] }, { "week": 2, "phaseId": "product-strategy", "nameZh": "用户价值与产品策略", "primaryQuestionZh": "为谁创造什么价值，先验证哪项假设？", "inputsZh": [ "问题卡与非 AI 基线", "一手用户或业务线索" ], "minimumArtifactsZh": [ "目标用户说明", "价值假设", "替代方案", "关键风险", "阶段范围与暂不做事项" ], "acceptanceGateZh": "至少一项优先级或价值假设因用户或业务证据被确认、修改或否定，并保存取舍理由。", "failureReturnZh": "继续访谈、观察或核对业务记录，不开始堆叠产品功能。", "handoffOutputsZh": [ "目标用户", "价值假设", "阶段范围", "被否定或待验证的假设" ], "evidenceIds": [] }, { "week": 3, "phaseId": "user-interaction", "nameZh": "交互与信任校准", "primaryQuestionZh": "用户何时信任、纠错、确认、退出或转人工？", "inputsZh": [ "阶段范围", "主要失败成本", "用户心智模型假设" ], "minimumArtifactsZh": [ "正常流程", "失败提示", "反馈与确认流程", "人工接管与退出路径" ], "acceptanceGateZh": "目标用户能完成任务，并能复述系统能做什么、不能做什么以及如何纠错或退出。", "failureReturnZh": "修改能力说明、失败提示、反馈、确认或人工接管流程后重新测试。", "handoffOutputsZh": [ "正常与失败流程", "交互约束", "需要数据或依据支持的节点" ], "evidenceIds": [ "google-pair-mental-models-20260720" ] }, { "week": 4, "phaseId": "data-context", "nameZh": "数据、上下文与知识边界", "primaryQuestionZh": "输出依据从哪里来，是否允许使用、具有代表性、保持更新且可追溯？", "inputsZh": [ "交互流程", "输入来源", "产品需要引用或使用的依据" ], "minimumArtifactsZh": [ "来源与用途登记", "许可或同意依据", "质量与冲突规则", "更新与删除责任", "需要检索时的切片、召回和依据链记录" ], "acceptanceGateZh": "来源、用途、许可、质量、冲突、更新和删除均有记录，并能从抽样输出追溯到允许使用的输入或资料。", "failureReturnZh": "移除不可解释或不允许的来源；补充覆盖缺口；需要检索时修正切片、召回或依据链。", "handoffOutputsZh": [ "可用数据", "上下文规则", "知识边界", "异常资料与处理规则" ], "evidenceIds": [ "google-pair-data-collection-evaluation-20260720" ] }, { "week": 5, "phaseId": "prototype-system", "nameZh": "原型与系统边界", "primaryQuestionZh": "模型、提示、检索、工具、权限和人工怎样分工？", "inputsZh": [ "正常与失败流程", "数据与知识边界", "系统约束" ], "minimumArtifactsZh": [ "可运行原型", "系统与数据流图", "权限边界", "错误分支", "延迟与成本记录" ], "acceptanceGateZh": "正常、边界、权限不足和依赖失败测试均可执行并留痕；系统按预期完成、拒绝、降级或转人工。", "failureReturnZh": "先补错误处理、权限控制、降级或人工接管，不扩展新功能。", "handoffOutputsZh": [ "原型版本", "系统图", "错误与成本记录", "待验证失败类型" ], "evidenceIds": [ "nist-ai-rmf-core-20260720" ] }, { "week": 6, "phaseId": "evaluation-experimentation", "nameZh": "冻结评测与发布门禁", "primaryQuestionZh": "什么结果才足以支持当前范围发布？", "inputsZh": [ "初始评测任务", "原型版本", "前五周失败记录" ], "minimumArtifactsZh": [ "版本化评测集", "指标卡", "运行前冻结的阈值", "逐条结果", "失败分类", "发布或阻断结论" ], "acceptanceGateZh": "在看结果前冻结评测集、指标和阈值；逐条结果可复核，严重失败与关键切片按预设规则决定是否阻断。", "failureReturnZh": "按失败原因回到问题定义、交互、数据或原型周修正，更新版本后重新运行同一核心评测。", "handoffOutputsZh": [ "冻结评测版本", "指标与阈值", "发布结论", "未解决风险" ], "evidenceIds": [ "google-pair-data-collection-evaluation-20260720", "nist-ai-rmf-core-20260720" ] }, { "week": 7, "phaseId": "delivery-adoption", "nameZh": "发布、协作与采用", "primaryQuestionZh": "产品怎样进入真实工作流，依赖和支持由谁负责？", "inputsZh": [ "发布结论", "未解决风险", "团队责任与外部依赖" ], "minimumArtifactsZh": [ "发布范围", "负责人和依赖", "培训与支持入口", "反馈计划", "停止与回退条件" ], "acceptanceGateZh": "完成一次发布就绪检查或预演，所有关键依赖、支持、反馈、停止和回退条件均有负责人。", "failureReturnZh": "缩小灰度范围，补齐责任、依赖或支持入口；关键条件无人负责时不得继续。", "handoffOutputsZh": [ "发布与采用计划", "就绪检查记录", "责任人", "剩余运行风险" ], "evidenceIds": [ "nist-ai-rmf-core-20260720" ] }, { "week": 8, "phaseId": "governance-operations", "nameZh": "治理演练与复盘", "primaryQuestionZh": "上线后谁监测、升级、处置、回滚和复审？", "inputsZh": [ "发布计划", "未解决风险", "监测与责任假设" ], "minimumArtifactsZh": [ "风险与负责人登记", "人工监督规则", "监测与告警", "事件升级", "降级或回滚", "复审日期" ], "acceptanceGateZh": "选择一个高影响失败，演练发现、升级、处置、通知、恢复和证据回流；所有步骤均有时间与责任记录。", "failureReturnZh": "回到产生缺口的对应周修正机制，更新评测集和责任记录后重新演练。", "handoffOutputsZh": [ "运行责任", "监测与回滚记录", "事件复盘", "下一轮最弱证据" ], "evidenceIds": [ "nist-ai-rmf-core-20260720", "google-responsible-ai-introduction-20260720" ] } ], "cycleAfterWeek8": { "methodZh": "按八个维度分别记录证据状态，不生成总体分数。下一轮只强化与目标项目相关且证据最弱的维度。", "requiredRecordFields": [ "phaseId", "artifactUrls", "validationRecordUrls", "failureExamples", "reviewer", "reviewedAt", "nextEvidenceNeeded" ], "stopRuleZh": "若关键风险无人负责、严重失败未关闭、输入来源不允许或结果不可复现，不应通过增加周数或工具名称把状态标为完成。" }, "title": "AI 产品经理八周执行路线图 v1", "description": "把同一个 AI 产品练习按问题、策略、交互、数据、原型、评测、发布和治理拆成八周，逐周记录输入、最低产物、验收门禁、失败回退和阶段交接证据。", "url": "https://course.dragonai.tech/datasets/ai-product-manager-eight-week-execution-roadmap-v1.json", "creator": "烛龙智元内容研究组" }