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Duration 14 hours
Course Outline
Introduction to Quality and Observability in WrenAI
- The significance of observability in AI-driven analytics
- Challenges associated with NL-to-SQL evaluation
- Frameworks for quality monitoring
Assessing NL-to-SQL Accuracy
- Defining success metrics for generated queries
- Establishing benchmarks and test datasets
- Automating evaluation pipelines
Prompt Tuning Methodologies
- Optimising prompts for precision and efficiency
- Domain adaptation through tuning
- Managing prompt libraries for enterprise usage
Monitoring Drift and Query Reliability
- Understanding query drift in production settings
- Tracking schema and data evolution
- Identifying anomalies in user queries
Logging Query History
- Recording and storing query history
- Utilising history for audits and troubleshooting
- Leveraging query insights to drive performance improvements
Monitoring and Observability Frameworks
- Integrating with monitoring tools and dashboards
- Metrics for reliability and accuracy
- Alerting and incident response procedures
Enterprise Implementation Strategies
- Scaling observability across teams
- Balancing accuracy 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
Summary and Next Steps
Requirements
- A solid grasp of data quality and reliability practices
- Proficiency in SQL and analytics workflows
- Familiarity with monitoring or observability tools
Audience
- Data reliability engineers
- BI leads
- QA specialists for analytics
