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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 NL to SQL conversions.
- Frameworks for monitoring quality.
Evaluating NL to SQL Accuracy
- Defining success metrics for generated queries.
- Setting up 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 applications.
Tracking Drift and Query Reliability
- Understanding query drift within production environments.
- Monitoring the evolution of schemas and data.
- Identifying anomalies in user queries.
Instrumenting Query History
- Logging and storing query history.
- Utilizing history for audits and troubleshooting.
- Leveraging query insights to drive performance improvements.
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 settings.
- 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
- Knowledge of data quality and reliability standards.
- Proficiency in SQL and analytics workflows.
- Familiarity with monitoring or observability platforms.
Target Audience
- Data reliability engineers.
- BI team leads.
- QA specialists for analytics.
14 Hours
