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Duration 14 hours
Course Outline
Foundations of Quality and Observability in WrenAI
- The importance of observability in AI-driven analytics
- Key challenges in evaluating NL to SQL outputs
- Establishing frameworks for quality monitoring
Assessing NL to SQL Accuracy
- Setting success criteria for generated queries
- Creating benchmarks and test datasets
- Automating evaluation pipelines
Prompt Tuning Methodologies
- Refining prompts for improved accuracy and efficiency
- Adapting to specific domains through tuning
- Maintaining prompt libraries for enterprise applications
Monitoring Drift and Query Consistency
- Analyzing query drift in production settings
- Tracking schema and data changes
- Identifying anomalies in user queries
Instrumenting Query History
- Recording and retaining query logs
- Utilizing historical data for audits and issue resolution
- Applying query insights to boost performance
Monitoring and Observability Architectures
- Connecting with monitoring tools and dashboards
- Defining metrics for reliability and accuracy
- Setting up alerting and incident response procedures
Enterprise Adoption Strategies
- Extending observability across teams
- Striking a balance between accuracy and performance in live systems
- Ensuring governance and accountability for AI-generated outputs
Future Trends in WrenAI Quality and Observability
- AI-enabled self-correction mechanisms
- Sophisticated evaluation frameworks
- Emerging features for enterprise-level observability
Recap and Road Ahead
Requirements
- A solid grasp of data quality and reliability standards
- Proficiency in SQL and analytics workflows
- Familiarity with monitoring or observability tools
Target Audience
- Data reliability engineers
- BI leadership
- QA specialists in analytics
