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Duration 14 hours
Course Outline
Introduction to Quality and Observability in WrenAI
- The importance of observability in AI-driven analytics.
- Challenges associated with evaluating NL to SQL.
- Frameworks for quality monitoring.
Assessing NL to SQL Accuracy
- Defining success criteria for generated queries.
- Creating benchmarks and test datasets.
- Automating evaluation pipelines.
Prompt Tuning Strategies
- Optimising prompts for precision and efficiency.
- Adapting to specific domains through tuning.
- Managing prompt libraries for enterprise applications.
Monitoring Drift and Query Consistency
- Understanding query drift in production settings.
- Tracking schema and data evolution.
- Identifying anomalies in user queries.
Recording Query History
- Logging and archiving query history.
- Utilising historical data for audits and troubleshooting.
- Applying query insights to drive performance enhancements.
Monitoring and Observability Frameworks
- Integrating with monitoring tools and dashboards.
- Key metrics for reliability and accuracy.
- Processes for alerting and incident response.
Enterprise Implementation Patterns
- Scaling observability across teams.
- Balancing accuracy and performance in production.
- Ensuring governance and accountability for AI outputs.
The Future of Quality and Observability in WrenAI
- AI-driven self-correction mechanisms.
- Advanced evaluation frameworks.
- Upcoming features for enterprise observability.
Conclusion and Next Steps
Requirements
- Proficiency in data quality and reliability standards.
- Experience with SQL and analytics workflows.
- Familiarity with monitoring or observability tools.
Target Audience
- Data reliability engineers.
- BI leads.
- QA specialists in analytics.
