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Duration 14 hours
Course Outline
Introduction to Quality and Observability in WrenAI
- The significance of observability in AI-driven analytics
- Key challenges in evaluating natural language to SQL
- Establishing frameworks for quality monitoring
Assessing NL to SQL Precision
- Defining success metrics for generated queries
- Setting up benchmarks and test datasets
- Automating evaluation workflows
Prompt Tuning Strategies
- Refining prompts for optimal accuracy and efficiency
- Adapting to specific domains through tuning
- Managing prompt libraries for enterprise environments
Monitoring Drift and Query Consistency
- Understanding query drift in production settings
- Tracking schema and data evolution
- Identifying anomalies in user queries
Instrumenting Query History
- Recording and archiving query history
- Utilizing historical data for audits and troubleshooting
- Applying query insights to drive performance enhancements
Monitoring and Observability Frameworks
- Integrating with monitoring tools and dashboards
- Key metrics for reliability and accuracy
- Establishing alerting and incident response procedures
Enterprise Deployment Patterns
- Scaling observability across multiple teams
- Balancing accuracy and performance in live operations
- Ensuring governance and accountability for AI outputs
The Future of Quality and Observability in WrenAI
- AI-powered self-correction mechanisms
- Advanced evaluation frameworks
- New features for enterprise observability
Recap and Next Steps
Requirements
- Solid grasp of data integrity and reliability protocols
- Proficiency in SQL and analytical workflows
- Working knowledge of monitoring or observability platforms
Target Audience
- Data reliability engineers
- Business Intelligence leaders
- QA specialists in analytics
