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Duration 14 hours
Course Outline
Foundations of Quality and Observability in WrenAI
- The significance of observability in AI-powered analytics
- Challenges associated with evaluating NL-to-SQL conversions
- Frameworks for monitoring quality metrics
Assessing NL-to-SQL Precision
- Defining success criteria for generated queries
- Setting up benchmarks and test datasets
- Automating evaluation workflows
Prompt Tuning Strategies
- Optimizing prompts for precision and efficiency
- Adapting to specific domains through tuning
- Managing prompt libraries for enterprise deployment
Monitoring Drift and Query Consistency
- Understanding query drift in live production environments
- Tracking schema changes and data evolution
- Identifying anomalies in user queries
Logging Query Histories
- Recording and archiving query history
- Utilizing history logs for audits and issue resolution
- Leveraging query insights to drive performance enhancements
Monitoring and Observability Architectures
- Integrating with monitoring tools and dashboards
- Key metrics for reliability and accuracy
- Establishing alerting and incident response protocols
Enterprise Adoption Patterns
- Scaling observability practices across teams
- Balancing precision and performance in production
- Ensuring governance and accountability for AI outputs
Future Trends in WrenAI Quality and Observability
- AI-enabled self-correction mechanisms
- Advanced evaluation frameworks
- Planned enhancements for enterprise observability
Conclusion and Recommended Next Steps
Requirements
- A solid grasp of data quality and reliability best practices
- Hands-on experience with SQL and analytics workflows
- Familiarity with monitoring or observability tools
Target Audience
- Data reliability engineers
- BI leads
- QA specialists in analytics
