name: product-management-human-data-platform
description: |
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.
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