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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 accuracy and efficiency.
- Domain adaptation through tuning.
- Managing prompt libraries for enterprise usage.
Tracking Drift and Query Reliability
- Understanding query drift in production environments.
- Monitoring schema and data evolution.
- Detecting anomalies in user queries.
Instrumenting Query History
- Logging and storing query history.
- Utilizing history for audits and troubleshooting.
- Leveraging query insights for performance improvements.
Monitoring and Observability Frameworks
- Integrating with monitoring tools and dashboards.
- Metrics for reliability and accuracy.
- Alerting and incident response processes.
Enterprise Implementation Patterns
- Scaling observability across 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
- Understanding of data quality and reliability practices.
- Experience with SQL and analytics workflows.
- Familiarity with monitoring or observability tools.
Target Audience
- Data reliability engineers.
- BI leads.
- QA professionals specializing in analytics.
14 Hours
