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Course Outline
Overview of Quality and Observability in WrenAI
- The importance of observability in AI-driven analytics
- Obstacles encountered in evaluating natural language to SQL
- Models for monitoring quality
Measuring NL to SQL Precision
- Setting success metrics for generated queries
- Creating benchmarks and test datasets
- Automating evaluation workflows
Methods for Prompt Tuning
- Refining prompts for enhanced accuracy and efficiency
- Adapting to specific domains through tuning
- Maintaining prompt libraries for enterprise applications
Monitoring Drift and Query Stability
- Comprehending query drift in live environments
- Observing schema and data changes
- Identifying anomalies in user-submitted queries
Instrumenting Query Logs
- Recording and archiving query history
- Utilizing historical data for audits and issue resolution
- Harnessing query insights for performance optimization
Supervision and Observability Architectures
- Connecting with monitoring tools and dashboards
- Key indicators for reliability and precision
- Alerting mechanisms and incident management procedures
Enterprise Adoption Models
- Expanding observability across multiple teams
- Striking a balance between accuracy and performance in production
- Governance and responsibility for AI-generated outputs
Future Trends in WrenAI Quality and Observability
- AI-powered self-correction systems
- Sophisticated evaluation models
- Emerging capabilities for enterprise-level observability
Recap and Future Directions
Requirements
- A solid grasp of data integrity and reliability standards
- Proficiency in SQL and analytics processes
- Knowledge of monitoring or observability platforms
Target Audience
- Data reliability engineers
- BI leaders
- QA experts in analytics
14 Hours
