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Course Outline
Introduction to Quality and Observability in WrenAI
- The importance of observability in AI-powered analytics
- Challenges associated with evaluating natural language to SQL processes
- 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
- Refining prompts for better accuracy and efficiency
- Adapting models to specific domains through tuning
- Managing prompt libraries for enterprise-scale use
Monitoring Drift and Query Reliability
- Understanding query drift in production environments
- Tracking changes in schema and underlying data
- Identifying anomalies in user query behavior
Instrumenting Query History
- Logging and archiving query history
- Utilizing historical data for audits and troubleshooting
- Applying query insights to drive performance enhancements
Monitoring and Observability Frameworks
- Connecting with monitoring tools and dashboards
- Key metrics for reliability and accuracy
- Processes for alerting and incident response
Enterprise Implementation Patterns
- Expanding observability across multiple teams
- Balancing accuracy requirements with production performance
- Establishing governance and accountability for AI-generated outputs
The Future of Quality and Observability in WrenAI
- AI-driven mechanisms for self-correction
- Advanced frameworks for evaluation
- Upcoming features tailored for enterprise observability
Summary and Next Steps
Requirements
- Knowledge of data quality and reliability standards
- Proficiency in SQL and analytical workflows
- Exposure to monitoring or observability platforms
Target Audience
- Data reliability engineers
- Business Intelligence leads
- Analytics quality assurance professionals
14 Hours
