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Duration 14 hours
Course Outline
Foundations of Quality and Observability in WrenAI
- The significance of observability in AI-based analytics
- Challenges associated with evaluating natural language to SQL
- Architectures for quality oversight
Assessing Natural Language to SQL Precision
- Setting success metrics for generated queries
- Creating benchmarks and test datasets
- Automating assessment workflows
Prompt Optimization Strategies
- Refining prompts for improved precision and efficiency
- Adapting to specific domains through tuning
- Administering prompt libraries for enterprise scenarios
Monitoring Drift and Query Stability
- Understanding query drift in production settings
- Tracking schema and data changes
- Identifying anomalies in user queries
Logging Query Histories
- Recording and archiving query logs
- Utilizing historical data for audits and debugging
- Applying query insights for performance enhancements
Oversight and Observability Architectures
- Connecting with monitoring tools and dashboards
- Indicators for reliability and precision
- Alerting mechanisms and incident response procedures
Enterprise Deployment Models
- Expanding observability across organizational teams
- Harmonizing precision and performance in live operations
- Governance and accountability for AI outputs
The Future of Quality and Observability in WrenAI
- AI-based self-correction systems
- Sophisticated assessment frameworks
- Upcoming capabilities for enterprise observability
Recap and Subsequent Actions
Requirements
- A solid grasp of data integrity and reliability standards
- Proficiency in SQL and analytics processes
- Experience with monitoring or observability platforms
Target Audience
- Data reliability specialists
- BI managers
- QA experts in analytics
