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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 data quality.
Evaluating NL to SQL Accuracy
- Establishing success criteria for generated queries.
- Setting up benchmarks and test datasets.
- Automating evaluation pipelines.
Prompt Optimization Techniques
- Optimizing prompts for improved accuracy and efficiency.
- Achieving domain adaptation through tuning.
- Managing prompt libraries for enterprise applications.
Tracking Drift and Query Reliability
- Understanding query drift in live production environments.
- Monitoring schema changes and data evolution.
- Detecting anomalies within user queries.
Instrumenting Query History
- Logging and archiving query history.
- Leveraging historical data for audits and troubleshooting.
- Utilizing query insights to drive performance improvements.
Monitoring and Observability Frameworks
- Integrating with monitoring tools and dashboards.
- Key metrics for assessing reliability and accuracy.
- Alerting mechanisms and incident response protocols.
Enterprise Implementation Patterns
- Scaling observability across multiple teams.
- Balancing accuracy and performance in production settings.
- Governance and accountability for AI-generated outputs.
The 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 best practices.
- Prior experience with SQL and analytics workflows.
- Familiarity with monitoring or observability tools.
Target Audience
- Data reliability engineers.
- Business Intelligence (BI) leads.
- QA professionals specializing in analytics.
14 Hours
