Product Management — Human Data Platform
When to Use
- 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)
When NOT to Use
- 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
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 |
Core Workflows
1. Vision, roadmap, and prioritization
Outcomes, segments, themes, RICE/ICE.
See references/roadmap_prioritization.md.
2. Annotation task and taxonomy design
Instructions, rubrics, schema, edge cases.
See references/annotation_task_design.md.
3. Quality systems
Gold sets, IAA, adjudication, rejection taxonomy.
See references/quality_systems.md.
4. Customer (ML team) delivery
Projects, pipelines, exports, SLAs.
See references/customer_ml_workflows.md.
5. Contributor and workforce product
Task UX, payments, trust, locale.
See references/contributor_workforce_product.md.
6. Privacy, ethics, and policy
PII, consent, retention, labor.
See references/privacy_ethics_policy.md.
Output standards
- 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
When to load references
- 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
1---2name: product-management-human-data-platform3description: Guides product management for human data platforms—annotation and labeling products, workforce workflows, task design, quality systems (gold sets, adjudication, inter-annotator agreement), customer ML-team project delivery, contributor experience, and privacy-safe handling of human-generated training data. Use when prioritizing roadmap for labeling/RLHF/eval data platforms, writing PRDs for annotation or QA features, defining success metrics for throughput and quality, scoping enterprise customer workflows, or balancing cost-quality-speed tradeoffs—not for hands-on model training (data-scientist), warehouse/analytics pipelines (data-warehouse-engineer), generic BRD workshops without product lens (business-analyst), AI solution architecture for copilots (applied-ai-architect-commercial-enterprise), or control implementation for audits (compliance-engineer). UX flows: product-designer. Eval harnesses: prompt-engineer-agent-prompts-evals. Pricing/packaging for platform: product-management-monetization.4---56# Product Management — Human Data Platform78## When to Use910- Define **vision, roadmap, and prioritization** for labeling, RLHF, or human-eval products11- Write **PRDs** for annotation UI, project setup, QA, workforce, or export/API features12- Design **annotation tasks** (taxonomy, instructions, rubrics, edge cases)13- Specify **quality programs**: gold tasks, consensus, adjudication, rejection reasons14- Scope **customer** workflows (ML teams): projects, batches, SLAs, delivery formats15- Improve **contributor/annotator** productivity, fairness, and trust/safety product surfaces16- Set **metrics**: throughput, quality, cost per label, time-to-delivery, contributor retention17- Partner on **privacy and ethics** requirements for human-submitted data (PII, consent, locale)1819## When NOT to Use2021- Facilitate generic process maps and BRDs without product ownership → `business-analyst`22- Wireframes and visual design only → `product-designer`23- RAG/copilot enterprise architecture → `applied-ai-architect-commercial-enterprise`24- Build eval harnesses and judges in code → `prompt-engineer-agent-prompts-evals`25- SOC/ISO evidence automation → `compliance-engineer`26- Data warehouse modeling → `data-warehouse-engineer`27- Cross-team delivery RAID without product discovery → `technical-program-manager`2829## Related skills3031| Need | Skill |32|---|---|33| BRD/user story format | `business-analyst` |34| Annotator and customer UX | `product-designer` |35| How labels feed model programs | `applied-ai-architect-commercial-enterprise` |36| Golden sets and regression evals | `prompt-engineer-agent-prompts-evals` |37| Privacy controls and audit evidence | `compliance-engineer` |38| Taxonomy/ontology for labels | `ontology-engineer` |39| Analytics for product teams | `analytics-data-engineering-manager-product` |4041## Core Workflows4243### 1. Vision, roadmap, and prioritization4445Outcomes, segments, themes, RICE/ICE.4647**See `references/roadmap_prioritization.md`.**4849### 2. Annotation task and taxonomy design5051Instructions, rubrics, schema, edge cases.5253**See `references/annotation_task_design.md`.**5455### 3. Quality systems5657Gold sets, IAA, adjudication, rejection taxonomy.5859**See `references/quality_systems.md`.**6061### 4. Customer (ML team) delivery6263Projects, pipelines, exports, SLAs.6465**See `references/customer_ml_workflows.md`.**6667### 5. Contributor and workforce product6869Task UX, payments, trust, locale.7071**See `references/contributor_workforce_product.md`.**7273### 6. Privacy, ethics, and policy7475PII, consent, retention, labor.7677**See `references/privacy_ethics_policy.md`.**7879## Output standards8081- PRDs state **persona, problem, success metrics, non-goals, and launch tier**82- Task specs include **worked examples** (gold, borderline, reject)83- Quality bar defined as **measurable thresholds**, not "high quality"84- Every feature maps to **cost, quality, or speed** lever85- Escalate legal/labor questions; do not ship policy in product copy alone8687## When to load references8889- **Roadmap** → `references/roadmap_prioritization.md`90- **Tasks** → `references/annotation_task_design.md`91- **Quality** → `references/quality_systems.md`92- **Customers** → `references/customer_ml_workflows.md`93- **Contributors** → `references/contributor_workforce_product.md`94- **Privacy** → `references/privacy_ethics_policy.md`
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