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Duration 14 hours
Course Outline
Foundations of Quality and Observability in WrenAI
- The role of observability in AI-based analytics
- Hurdles in assessing natural language to SQL conversions
- Structures for quality oversight
Assessing NL-to-SQL Precision
- Setting success metrics for generated queries
- Creating benchmarks and test datasets
- Automating evaluation workflows
Prompt Optimization Strategies
- Refining prompts for improved accuracy and efficiency
- Adapting to specific domains through tuning
- Managing enterprise prompt libraries
Monitoring Drift and Query Stability
- Grasping query drift within production environments
- Tracking schema and data changes
- Identifying anomalies in user queries
Recording Query History
- Logging and archiving query records
- Utilizing history for audits and debugging
- Using query insights to boost performance
Monitoring and Observability Systems
- Connecting with monitoring tools and dashboards
- Defining metrics for stability and accuracy
- Managing alerts and incident response
Enterprise Deployment Models
- Expanding observability across teams
- Balancing accuracy with performance in live settings
- Governance and accountability for AI results
The Future of Quality and Observability in WrenAI
- AI-based self-correction systems
- Sophisticated evaluation structures
- Future enterprise observability capabilities
Conclusions and Recommendations
Requirements
- Knowledge of data integrity and reliability standards
- Proficiency in SQL and analytical workflows
- Exposure to monitoring or observability platforms
Target Audience
- Data reliability engineers
- BI leadership
- Quality assurance specialists in analytics
