product-management-human-data-platform

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🇺🇸

Original

English

🇨🇳

Translation

Chinese

Product Management — Human Data Platform

产品管理——人类数据平台

When to Use

使用场景

Define vision, roadmap, and prioritization for labeling, RLHF, or human-eval products
Write PRDs for annotation UI, project setup, QA, workforce, or export/API features
Design annotation tasks (taxonomy, instructions, rubrics, edge cases)
Specify quality programs: gold tasks, consensus, adjudication, rejection reasons
Scope customer workflows (ML teams): projects, batches, SLAs, delivery formats
Improve contributor/annotator productivity, fairness, and trust/safety product surfaces
Set metrics: throughput, quality, cost per label, time-to-delivery, contributor retention
Partner on privacy and ethics requirements for human-submitted data (PII, consent, locale)

为标注、RLHF或人工评估类产品定义愿景、路线图及优先级
为标注UI、项目配置、QA、人员管理或导出/API功能撰写PRD
设计标注任务（分类体系、操作指南、评分标准、边缘案例）
制定质量方案：黄金任务、共识机制、裁决流程、驳回原因分类
规划**客户（ML团队）**工作流：项目管理、批次处理、服务水平协议（SLA）、交付格式
提升贡献者/标注员的生产力、公平性，优化信任与安全相关产品界面
设置指标：吞吐量、质量、单标签成本、交付周期、贡献者留存率
协作落实人工提交数据的隐私与伦理要求（PII、授权同意、地域适配）

When NOT to Use

不适用场景

Facilitate generic process maps and BRDs without product ownership →
```
business-analyst
```
Wireframes and visual design only →
```
product-designer
```

RAG/copilot enterprise architecture →

applied-ai-architect-commercial-enterprise

Build eval harnesses and judges in code →
```
prompt-engineer-agent-prompts-evals
```
SOC/ISO evidence automation →
```
compliance-engineer
```
Data warehouse modeling →
```
data-warehouse-engineer
```
Cross-team delivery RAID without product discovery →
```
technical-program-manager
```

无产品所有权的通用流程梳理与BRD制定 →
```
business-analyst
```
仅负责线框图与视觉设计 →
```
product-designer
```

RAG/copilot企业架构设计 →

applied-ai-architect-commercial-enterprise

代码实现评估工具与判定逻辑 →
```
prompt-engineer-agent-prompts-evals
```
SOC/ISO证据自动化 →
```
compliance-engineer
```
数据仓库建模 →
```
data-warehouse-engineer
```
无产品调研的跨团队交付RAID管理 →
```
technical-program-manager
```

Related skills

Need	Skill
BRD/user story format	`business-analyst`
Annotator and customer UX	`product-designer`
How labels feed model programs	`applied-ai-architect-commercial-enterprise`
Golden sets and regression evals	`prompt-engineer-agent-prompts-evals`
Privacy controls and audit evidence	`compliance-engineer`
Taxonomy/ontology for labels	`ontology-engineer`
Analytics for product teams	`analytics-data-engineering-manager-product`

需求	技能岗位
BRD/用户故事格式撰写	`business-analyst`
标注员与客户体验设计	`product-designer`
标签如何赋能模型方案	`applied-ai-architect-commercial-enterprise`
黄金数据集与回归评估	`prompt-engineer-agent-prompts-evals`
隐私控制与审计证据	`compliance-engineer`
标签分类体系/本体设计	`ontology-engineer`
产品团队数据分析	`analytics-data-engineering-manager-product`

Core Workflows

核心工作流

1. Vision, roadmap, and prioritization

1. 愿景、路线图与优先级制定

Outcomes, segments, themes, RICE/ICE.

See
references/roadmap_prioritization.md
.

成果、细分领域、核心主题、RICE/ICE模型。

详见
references/roadmap_prioritization.md
。

2. Annotation task and taxonomy design

2. 标注任务与分类体系设计

Instructions, rubrics, schema, edge cases.

See
references/annotation_task_design.md
.

操作指南、评分标准、数据 schema、边缘案例。

详见
references/annotation_task_design.md
。

3. Quality systems

3. 质量体系搭建

Gold sets, IAA, adjudication, rejection taxonomy.

See
references/quality_systems.md
.

黄金数据集、标注者间一致性（IAA）、裁决流程、驳回分类体系。

详见
references/quality_systems.md
。

4. Customer (ML team) delivery

4. 客户（ML团队）交付管理

Projects, pipelines, exports, SLAs.

See
references/customer_ml_workflows.md
.

项目、管道、导出、服务水平协议（SLA）。

详见
references/customer_ml_workflows.md
。

5. Contributor and workforce product

5. 贡献者与人员产品管理

Task UX, payments, trust, locale.

See
references/contributor_workforce_product.md
.

任务用户体验、薪酬、信任机制、地域适配。

详见
references/contributor_workforce_product.md
。

6. Privacy, ethics, and policy

6. 隐私、伦理与政策合规

PII, consent, retention, labor.

See
references/privacy_ethics_policy.md
.

PII、授权同意、数据留存、劳工相关规范。

详见
references/privacy_ethics_policy.md
。

Output standards

输出标准

PRDs state persona, problem, success metrics, non-goals, and launch tier
Task specs include worked examples (gold, borderline, reject)
Quality bar defined as measurable thresholds, not "high quality"
Every feature maps to cost, quality, or speed lever
Escalate legal/labor questions; do not ship policy in product copy alone

PRD需明确用户角色、问题、成功指标、非目标及发布层级
任务规范需包含实例示例（黄金案例、边界案例、驳回案例）
质量标准需定义为可衡量阈值，而非模糊的“高质量”
每个功能需对应成本、质量或速度优化维度
法律/劳工相关问题需升级处理，不可仅通过产品文案制定政策

When to load references

何时加载参考资料

Roadmap →
```
references/roadmap_prioritization.md
```
Tasks →
```
references/annotation_task_design.md
```
Quality →
```
references/quality_systems.md
```
Customers →
```
references/customer_ml_workflows.md
```

Contributors →

references/contributor_workforce_product.md

Privacy →
```
references/privacy_ethics_policy.md
```

路线图制定 →
```
references/roadmap_prioritization.md
```
任务设计 →
```
references/annotation_task_design.md
```
质量体系搭建 →
```
references/quality_systems.md
```
客户工作流规划 →
```
references/customer_ml_workflows.md
```

贡献者管理 →

references/contributor_workforce_product.md

隐私合规 →
```
references/privacy_ethics_policy.md
```