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Course Outline
Introduction to Quality and Observability in WrenAI
- Why observability matters in AI-driven analytics
- Challenges in NL to SQL evaluation
- Frameworks for 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 accuracy and efficiency
- Domain adaptation through tuning
- Managing prompt libraries for enterprise use
Tracking Drift and Query Reliability
- Understanding query drift in production
- Monitoring schema and data evolution
- Detecting anomalies in user queries
Instrumenting Query History
- Logging and storing query history
- Using history for audits and troubleshooting
- Leveraging query insights for performance improvements
Monitoring and Observability Frameworks
- Integrating with monitoring tools and dashboards
- Metrics for reliability and accuracy
- Alerting and incident response processes
Enterprise Implementation Patterns
- Scaling observability across teams
- Balancing accuracy and performance in production
- Governance and accountability for AI outputs
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
- An understanding of data quality and reliability practices
- Experience with SQL and analytics workflows
- Familiarity with monitoring or observability tools
Audience
- Data reliability engineers
- BI leads
- QA professionals for analytics
14 Hours
