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Duration 14 hours
Course Outline
Overview of Quality and Observability in WrenAI
- The significance of observability in AI-driven analytics
- Challenges associated with evaluating NL to SQL conversions
- Frameworks for quality monitoring
Assessing NL to SQL Precision
- Defining success metrics for generated queries
- Establishing benchmarks and test datasets
- Automating evaluation pipelines
Prompt Tuning Strategies
- Optimizing prompts for precision and efficiency
- Domain-specific adaptation through tuning
- Managing prompt libraries for enterprise applications
Monitoring Drift and Query Stability
- Understanding query drift in production settings
- Monitoring schema and data evolution
- Identifying anomalies in user queries
Instrumenting Query History
- Logging and archiving query history
- Utilizing history for audits and issue resolution
- Leveraging query insights for performance enhancements
Monitoring and Observability Frameworks
- Integration with monitoring tools and dashboards
- Metrics for reliability and precision
- Alerting and incident response procedures
Enterprise Implementation Strategies
- Scaling observability across teams
- Balancing precision and performance in production
- Governance and accountability for AI outputs
The Future of Quality and Observability in WrenAI
- AI-driven self-correction mechanisms
- Advanced evaluation frameworks
- Upcoming features for enterprise observability
Recap and Future Directions
Requirements
- A solid grasp of data quality and integrity best practices
- Proficiency in SQL and analytics workflows
- Experience with monitoring or observability tools
Target Audience
- Data reliability engineers
- BI leaders
- QA specialists in analytics
