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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 natural language to SQL outputs
- Frameworks for monitoring data quality
Evaluating NL to SQL Accuracy
- Defining success criteria for generated queries
- Establishing benchmarks and preparing test datasets
- Automating evaluation pipelines
Prompt Tuning Techniques
- Optimizing prompts for enhanced accuracy and efficiency
- Adapting to specific domains through tuning
- Managing prompt libraries for enterprise-scale use
Tracking Drift and Query Reliability
- Understanding query drift within production environments
- Monitoring schema changes and data evolution
- Detecting anomalies in user queries
Instrumenting Query History
- Logging and storing query history for analysis
- Utilizing historical data for audits and troubleshooting
- Leveraging query insights to drive performance improvements
Monitoring and Observability Frameworks
- Integrating with existing monitoring tools and dashboards
- Key metrics for assessing reliability and accuracy
- Establishing alerting and incident response protocols
Enterprise Implementation Patterns
- Scaling observability practices across multiple teams
- Balancing accuracy requirements with production performance
- Governance and accountability measures for AI outputs
Future of Quality and Observability in WrenAI
- AI-driven self-correction mechanisms
- Advanced evaluation frameworks on the horizon
- New features upcoming for enterprise observability
Summary and Next Steps
Requirements
- Foundational knowledge of data quality and reliability standards
- Proficiency in SQL and experience with analytics workflows
- Familiarity with monitoring or observability platforms
Target Audience
- Data reliability engineers
- Business Intelligence (BI) team leaders
- Quality assurance specialists for analytics solutions
14 Hours
