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Duration 14 hours
Course Outline
Intro to WrenAI Quality and Observability
- The significance of observability in AI-powered analytics
- Key challenges in assessing NL-to-SQL generation
- Structured frameworks for quality monitoring
Assessing NL-to-SQL Precision
- Setting success metrics for generated queries
- Creating benchmarks and test datasets
- Building automated evaluation pipelines
Prompt Tuning Strategies
- Refining prompts for improved accuracy and efficiency
- Adapting models to specific domains through tuning
- Maintaining prompt libraries for enterprise scenarios
Monitoring Drift and Query Consistency
- Recognizing query drift in live environments
- Tracking schema and data changes
- Identifying anomalies in user queries
Logging Query History
- Recording and archiving query history
- Leveraging history for audits and troubleshooting
- Using query insights to boost performance
Observability and Monitoring Architectures
- Connecting with monitoring tools and dashboards
- Defining metrics for reliability and accuracy
- Establishing alerting and incident response workflows
Enterprise Deployment Patterns
- Scaling observability across multiple teams
- Balancing accuracy with performance in production
- Ensuring governance and accountability for AI results
Future Trends in WrenAI Quality and Observability
- AI-driven self-correction capabilities
- Sophisticated evaluation frameworks
- Planned enhancements for enterprise observability
Recap and Future Actions
Requirements
- Solid grasp of data quality and reliability standards
- Proficiency in SQL and analytics workflows
- Familiarity with monitoring or observability tools
Target Audience
- Data reliability engineers
- BI leaders
- QA specialists in analytics
