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Duration 14 hours
Course Outline
Introduction to Quality and Observability in WrenAI
- The importance of observability in AI-enabled analytics
- Challenges in evaluating NL to SQL conversions
- Frameworks for monitoring quality
Assessing NL to SQL Precision
- Defining success metrics for generated queries
- Creating benchmarks and test datasets
- Automating evaluation workflows
Prompt Optimization Techniques
- Refining prompts for precision and efficiency
- Domain-specific adaptation through tuning
- Managing prompt libraries for enterprise applications
Tracking Drift and Query Consistency
- Understanding query drift in live environments
- Monitoring schema and data changes
- Identifying anomalies in user queries
Logging Query History
- Recording and archiving query history
- Utilizing history for audits and troubleshooting
- Leveraging query insights for performance enhancement
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-based self-correction mechanisms
- Advanced evaluation frameworks
- New features for enterprise observability
Recap and Next Steps
Requirements
- Familiarity with data integrity and reliability standards
- Proficiency in SQL and analytical workflows
- Experience with monitoring or observability tools
Target Audience
- Data reliability engineers
- BI managers
- QA specialists in analytics
