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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 conversions
- Frameworks for monitoring data quality
Assessing NL to SQL Accuracy
- Defining success metrics for generated queries
- Setting up benchmarks and test datasets
- Automating evaluation workflows
Prompt Tuning Techniques
- Optimizing prompts for both accuracy and efficiency
- Adapting to specific domains through tuning
- Managing prompt libraries for enterprise applications
Tracking Drift and Query Reliability
- Understanding query drift within production environments
- Monitoring changes in schema and data structures
- Identifying anomalies in user-generated queries
Instrumenting Query History
- Logging and archiving query history
- Utilizing historical data for audits and troubleshooting
- Leveraging query insights to drive performance enhancements
Monitoring and Observability Frameworks
- Integrating with existing monitoring tools and dashboards
- Key metrics for ensuring reliability and accuracy
- Procedures for alerting and incident response
Enterprise Implementation Patterns
- Expanding observability capabilities across teams
- Balancing accuracy requirements with production performance
- Establishing governance and accountability for AI outputs
The Future of Quality and Observability in WrenAI
- AI-driven self-correction mechanisms
- Next-generation evaluation frameworks
- Upcoming features dedicated to enterprise observability
Summary and Next Steps
Requirements
- Familiarity with data quality and reliability protocols
- Prior experience with SQL and analytics workflows
- Knowledge of monitoring or observability tools
Target Audience
- Data reliability engineers
- BI team leads
- QA specialists focused on analytics
14 Hours
