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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 outputs.
- Frameworks for quality monitoring.
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 both accuracy and efficiency.
- Achieving domain adaptation through tuning.
- Managing prompt libraries for enterprise usage.
Tracking Drift and Query Reliability
- Understanding query drift within production environments.
- Monitoring schema and data evolution.
- Detecting anomalies in user-generated queries.
Instrumenting Query History
- Logging and storing query history.
- Utilizing historical data for audits and troubleshooting.
- Leveraging query insights for 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 multiple teams.
- Balancing accuracy and performance in production environments.
- Governance and accountability for AI-generated outputs.
Future of Quality and Observability in WrenAI
- AI-driven self-correction mechanisms.
- Advanced evaluation frameworks.
- Upcoming features tailored for enterprise observability.
Summary and Next Steps
Requirements
- A solid understanding of data quality and reliability standards.
- Proficiency with SQL and analytics workflows.
- Familiarity with monitoring or observability tools.
Target Audience
- Data reliability engineers.
- Business Intelligence (BI) team leads.
- Quality Assurance specialists within analytics domains.
14 Hours
