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Duration 14 hours
Course Outline
Foundations of Quality & Observability in WrenAI
- The importance of observability in AI-based analytics.
- Key challenges in evaluating NL-to-SQL systems.
- Structures for quality monitoring.
Assessing NL-to-SQL Precision
- Setting success metrics for generated queries.
- Creating benchmarks and test datasets.
- Streamlining evaluation workflows.
Strategies for Prompt Optimization
- Refining prompts for better accuracy and efficiency.
- Adapting to specific domains through tuning.
- Maintaining prompt libraries for corporate use.
Monitoring Drift & Query Stability
- Analyzing query drift in live settings.
- Tracking changes in schemas and data.
- Identifying unusual user query patterns.
Implementing Query History Logging
- Recording and archiving query activity.
- Using historical data for audits and issue resolution.
- Applying query insights to boost performance.
Deploying Monitoring & Observability Frameworks
- Connecting with monitoring tools and dashboards.
- Defining metrics for reliability and precision.
- Establishing alerting and incident management procedures.
Corporate Deployment Patterns
- Expanding observability across multiple teams.
- Optimizing the balance between accuracy and speed in production.
- Ensuring governance and accountability for AI results.
Evolution of Quality & Observability in WrenAI
- Self-correcting mechanisms powered by AI.
- Next-generation evaluation frameworks.
- Future enterprise observability features.
Wrap-up & Future Directions
Requirements
- Conceptual understanding of data quality and reliability standards.
- Hands-on experience with SQL and analytical workflows.
- Knowledge of monitoring or observability tools.
Target Audience
- Data reliability engineers.
- Business Intelligence (BI) leaders.
- Quality Assurance (QA) specialists in analytics.
