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Course Outline
Introduction to Quality and Observability in WrenAI
- The importance of observability in AI-driven analytics
- Challenges associated with NL to SQL evaluation
- Frameworks for maintaining quality monitoring
Evaluating NL to SQL Accuracy
- Defining success criteria for generated queries
- Establishing benchmarks and 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 usage
Tracking Drift and Query Reliability
- Understanding query drift in production environments
- Monitoring schema changes and data evolution
- Detecting anomalies within user queries
Instrumenting Query History
- Logging and storing query history
- Leveraging history for audits and troubleshooting
- Utilizing query insights to drive performance improvements
Monitoring and Observability Frameworks
- Integrating with monitoring tools and dashboards
- Key metrics for reliability and accuracy
- Alerting mechanisms and incident response protocols
Enterprise Implementation Patterns
- Scaling observability across multiple teams
- Balancing accuracy and performance in production
- Establishing governance and accountability for AI outputs
The 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
- Foundational knowledge of data quality and reliability standards
- Practical experience with SQL and analytics workflows
- Familiarity with monitoring or observability platforms
Target Audience
- Data reliability engineers
- Business Intelligence (BI) leads
- QA professionals specializing in analytics
14 Hours
