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Duration 14 hours
Course Outline
Introduction to Quality and Observability in WrenAI
- The significance of observability in AI-powered analytics.
- Key challenges in evaluating Natural Language to SQL.
- Frameworks for quality monitoring.
Assessing NL to SQL Accuracy
- Defining success metrics for generated queries.
- Creating benchmarks and test datasets.
- Automating evaluation pipelines.
Prompt Tuning Strategies
- Refining prompts for enhanced accuracy and efficiency.
- Adapting to specific domains through tuning.
- Managing prompt libraries for enterprise applications.
Monitoring Drift and Query Consistency
- Analyzing query drift in production settings.
- Tracking changes in schema and data.
- Identifying anomalies in user queries.
Instrumenting Query History
- Logging and retaining query history.
- Utilizing historical data for auditing and troubleshooting.
- Applying query insights to drive performance gains.
Monitoring and Observability Frameworks
- Integration with monitoring tools and dashboards.
- Key metrics for reliability and accuracy.
- Alerting mechanisms and incident response procedures.
Enterprise Deployment Patterns
- Scaling observability initiatives across teams.
- Balancing accuracy with performance in live systems.
- Ensuring governance and accountability for AI outputs.
The Future of Quality and Observability in WrenAI
- AI-based self-correction mechanisms.
- Next-generation evaluation frameworks.
- Emerging features for enterprise observability.
Recap and Future Steps
Requirements
- Foundational knowledge of data quality and reliability standards.
- Proficiency in SQL and analytics workflows.
- Experience with monitoring or observability tools.
Target Audience
- Data reliability engineers.
- Business Intelligence (BI) leads.
- QA specialists in analytics.
