Use Cases
Training Data Collection
Comprehensive guide for collecting high-quality training data to improve AI systems
Overview
High-quality training data is essential for developing robust AI systems. This guide covers best practices for collecting, curating, and leveraging human-generated training data.Key Components
Detailed traces of human expert reasoning processes
Programming examples and solutions
Structured feedback from domain experts
Collection Methods
Interactive Collection
Gather data through direct expert interaction:- Real-time problem solving sessions
- Structured interviews and walkthroughs
- Collaborative debugging sessions
- Pair programming exercises
Passive Collection
Automated collection from expert workflows:- IDE plugins tracking coding patterns
- Browser extensions logging research paths
- Screen recording with audio annotations
- Git commit message analysis
Hybrid Approaches
Combine multiple collection methods:- Expert review of automated collections
- AI-assisted expert annotations
- Collaborative filtering of examples
- Peer validation workflows
Quality Control
Multi-stage validation pipeline
Integration
API endpoints for data collection
Best Practices
- Document full context for each example
- Capture edge cases and failure modes
- Include negative examples
- Maintain consistent formatting
- Version control all data
- Regular quality audits
- Diverse expert representation
Expert Network
Access to qualified domain experts:- Software engineers
- ML researchers
- Domain specialists
- Quality assurance
- Technical writers
- Legal experts
- Medical professionals
Security & Privacy
- End-to-end encryption
- Access controls
- Data anonymization
- Audit logging
- Compliance tracking
- Secure storage
- Regular audits