🔴 AI FIRST Quality Principle
Apply the AI FIRST principle: never accept first-pass quality. Minimum 2 iterations. Read all output, improve every section. No shortcuts.
Purpose
Expert knowledge in designing robust ETL (Extract, Transform, Load) pipelines for automated data processing, focusing on reliability, monitoring, and maintainability.
Core Principles
- Idempotency - Pipeline runs produce same results
- Observability - Full visibility into pipeline health
- Error Recovery - Graceful handling of failures
- Version Tracking - Track all data changes
- Monitoring - Real-time pipeline health checks
Enforces
- ETL workflow patterns (Extract → Transform → Load)
- Automated scheduling (cron, GitHub Actions)
- Data versioning and archival
- Pipeline health monitoring
- Error recovery strategies
- Audit logging
When to Use
- Building automated data pipelines
- Scheduling data fetching workflows
- Implementing data versioning
- Monitoring pipeline health
- Designing error recovery
References
Version: 1.0 | Last Updated: 2026-02-06 | Category: Development & Operations