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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