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Duration 14 hours
Course Outline
Foundations of Quality and Observability in WrenAI
- The significance of observability in AI-powered analytics
- Obstacles in assessing NL to SQL outputs
- Structural frameworks for quality oversight
Measuring NL to SQL Precision
- Setting success benchmarks for generated queries
- Creating reference points and test datasets
- Streamlining evaluation workflows
Prompt Tuning Strategies
- Enhancing prompts for precision and efficiency
- Tailoring prompts to specific domains through tuning
- Managing prompt repositories for enterprise applications
Monitoring Drift and Query Consistency
- Recognizing query drift within production settings
- Tracking changes in schemas and data evolution
- Identifying irregularities in user-submitted queries
Documenting Query History
- Recording and archiving query logs
- Utilizing historical data for audits and issue resolution
- Applying query insights to drive performance gains
Monitoring and Observability Architectures
- Connecting with monitoring tools and dashboards
- Defining metrics for reliability and accuracy
- Establishing alerting and incident management workflows
Enterprise Deployment Models
- Extending observability across teams
- Striking a balance between accuracy and performance in live environments
- Ensuring governance and responsibility for AI results
The Future of Quality and Observability in WrenAI
- AI-enabled self-correction systems
- Sophisticated evaluation structures
- Emerging features for enterprise-level observability
Recap and Recommended Actions
Requirements
- Solid grasp of data quality and reliability standards
- Proficiency in SQL and analytics processes
- Working knowledge of monitoring and observability platforms
Target Audience
- Data reliability engineers
- BI managers
- QA specialists in analytics
