Course Outline

Introduction to Quality and Observability in WrenAI

  • Why observability matters in AI-driven analytics
  • Challenges in NL to SQL evaluation
  • 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 accuracy and efficiency
  • Domain adaptation through tuning
  • Managing prompt libraries for enterprise use

Tracking Drift and Query Reliability

  • Understanding query drift in production
  • Monitoring schema and data evolution
  • Detecting anomalies in user queries

Instrumenting Query History

  • Logging and storing query history
  • Using 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

  • An understanding of data quality and reliability practices
  • Experience with SQL and analytics workflows
  • Familiarity with monitoring or observability tools

Audience

  • Data reliability engineers
  • BI leads
  • QA professionals for analytics
 14 Hours

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