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Course Outline
Introduction to Quality and Observability in WrenAI
- The importance of observability in AI-driven analytics
- Challenges associated with evaluating natural language to SQL conversions
- Frameworks for monitoring quality
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 improved accuracy and efficiency
- Achieving domain adaptation through tuning
- Managing prompt libraries for enterprise applications
Tracking Drift and Query Reliability
- Understanding query drift within production environments
- Monitoring the evolution of schemas and data
- Detecting anomalies in user queries
Instrumenting Query History
- Logging and storing query history
- Utilizing history for audits and troubleshooting
- Leveraging query insights to drive performance improvements
Monitoring and Observability Frameworks
- Integrating with monitoring tools and dashboards
- Metrics for ensuring reliability and accuracy
- Alerting and incident response procedures
Enterprise Implementation Patterns
- Scaling observability across various 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
- Familiarity with data quality and reliability practices
- Experience working with SQL and analytics workflows
- Knowledge of monitoring or observability tools
Audience
- Data reliability engineers
- BI leads
- QA professionals specializing in analytics
14 Hours
