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Duration 14 hours
Course Outline
Overview of Quality and Observability in WrenAI
- The importance of observability in AI-based analytics
- Challenges associated with NL-to-SQL assessment
- Structures for quality tracking
Assessing NL-to-SQL Precision
- Establishing success parameters for generated queries
- Setting up benchmarks and test datasets
- Automating assessment workflows
Prompt Optimization Methods
- Refining prompts for better precision and efficiency
- Adapting to specific domains through tuning
- Administering prompt libraries for enterprise applications
Monitoring Drift and Query Dependability
- Recognizing query drift in live environments
- Tracking schema and data changes
- Identifying irregularities in user queries
Recording Query Logs
- Logging and archiving query history
- Utilizing historical data for audits and issue resolution
- Leveraging query insights to boost performance
Monitoring and Observability Structures
- Integration with monitoring tools and dashboards
- Metrics for dependability and precision
- Alerting systems and incident management workflows
Enterprise Deployment Strategies
- Scaling observability across departments
- Balancing precision with performance in production
- Governance and responsibility for AI outputs
The Future of Quality and Observability in WrenAI
- AI-based self-correction features
- Sophisticated assessment structures
- Upcoming capabilities for enterprise observability
Wrap-up and Recommendations
Requirements
- Proficiency in data quality and reliability standards
- Proficiency with SQL and analytics processes
- Knowledge of monitoring or observability platforms
Intended Audience
- Data reliability engineers
- BI leaders
- QA specialists in analytics
