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Course Outline
Introduction to Quality and Observability in WrenAI
- The importance of observability in AI-driven analytics for government operations
- Challenges in evaluating natural language to SQL transformations
- Frameworks for monitoring quality in AI systems
Evaluating NL to SQL Accuracy
- Establishing criteria for the success of generated queries
- Setting benchmarks and test datasets for evaluation
- Automating pipelines to assess query accuracy
Prompt Tuning Techniques
- Enhancing prompts for improved accuracy and efficiency
- Adapting prompts to specific domains through tuning
- Managing prompt libraries for enterprise-level use
Tracking Drift and Query Reliability
- Understanding and addressing query drift in production environments
- Monitoring changes in data schemas and content
- Identifying anomalies in user-generated queries
Instrumenting Query History
- Logging and maintaining records of query history
- Utilizing query logs for audits and troubleshooting purposes
- Using insights from query data to enhance performance
Monitoring and Observability Frameworks
- Integrating with monitoring tools and dashboards for government use
- Defining metrics to measure reliability and accuracy
- Implementing alerting and incident response procedures
Enterprise Implementation Patterns
- Scaling observability practices across multiple teams
- Balancing accuracy and performance in production systems for government applications
- Ensuring governance and accountability in AI-generated outputs
Future of Quality and Observability in WrenAI
- Developing AI-driven self-correction mechanisms
- Advancing evaluation frameworks for enhanced accuracy
- Introducing new features to support enterprise-level observability
Summary and Next Steps
Requirements
- An understanding of data quality and reliability practices for government
- Experience with SQL and analytics workflows
- Familiarity with monitoring or observability tools
Audience
- Data reliability engineers
- BI leads
- QA professionals for government analytics
14 Hours
