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Course Outline
Introduction to Quality and Observability in WrenAI
- The importance of observability in AI-driven analytics
- Challenges associated with evaluating NL to SQL outputs
- Overview of quality monitoring frameworks
Evaluating NL to SQL Accuracy
- Establishing success criteria for generated queries
- Setting up benchmarks and test datasets
- Automating evaluation pipelines
Prompt Tuning Techniques
- Optimizing prompts for better accuracy and efficiency
- Adapting domains through effective tuning
- Managing prompt libraries for enterprise-scale use
Tracking Drift and Query Reliability
- Understanding query drift in production settings
- Monitoring schema and data evolution
- Identifying anomalies in user queries
Instrumenting Query History
- Logging and storing query history
- Leveraging history for audits and troubleshooting
- Using query insights to drive performance improvements
Monitoring and Observability Frameworks
- Integrating with monitoring tools and dashboards
- Key metrics for reliability and accuracy
- Alerting and incident response procedures
Enterprise Implementation Patterns
- Scaling observability across multiple teams
- Balancing accuracy and performance in production
- Governance and accountability for AI outputs
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
- Familiarity with data quality and reliability best practices
- Practical experience with SQL and analytics workflows
- Knowledge of monitoring and observability tools
Target Audience
- Data reliability engineers
- Business Intelligence (BI) leads
- Analytics QA professionals
14 Hours
