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Course Outline
Introduction to Quality and Observability in WrenAI
- The importance of observability in AI-centric analytics.
- Key challenges in evaluating natural language to SQL outputs.
- Overview of frameworks for quality monitoring.
Assessing NL to SQL Accuracy
- Defining clear success criteria for generated queries.
- Setting up benchmarks and test datasets.
- Automating evaluation pipelines for efficiency.
Prompt Optimization Strategies
- Refining prompts for enhanced accuracy and speed.
- Adapting to specific domains through tuning.
- Managing prompt libraries for enterprise scalability.
Tracking Drift and Query Reliability
- Understanding query drift within production environments.
- Monitoring schema changes and data evolution.
- Identifying anomalies in user query patterns.
Instrumenting Query History
- Logging and storing historical query data.
- Utilizing history for audits and troubleshooting.
- Leveraging query insights to drive performance improvements.
Monitoring and Observability Frameworks
- Integrating with existing monitoring tools and dashboards.
- Key metrics for measuring reliability and accuracy.
- Establishing alerting and incident response protocols.
Enterprise Implementation Patterns
- Scaling observability practices across multiple teams.
- Balancing accuracy requirements with production performance.
- Ensuring governance and accountability for AI-generated outputs.
The Future of Quality and Observability in WrenAI
- AI-driven mechanisms for self-correction.
- Emerging advanced evaluation frameworks.
- Upcoming features designed for enterprise observability.
Summary and Next Steps
Requirements
- A solid grasp of data quality and reliability principles.
- Practical experience with SQL and analytics workflows.
- Familiarity with monitoring or observability platforms.
Target Audience
- Data reliability engineers.
- Business Intelligence (BI) leaders.
- QA specialists focused on analytics.
14 Hours
