Quality and Observability for WrenAI: Evaluation, Prompt Tuning, and Monitoring Training Course

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 (NL to SQL) conversions.
• Frameworks and methodologies for ensuring quality monitoring.

Evaluating NL to SQL Accuracy

• Establishing clear success criteria for generated queries in government applications.
• Developing benchmarks and test datasets for accurate evaluation.
• Automating evaluation pipelines to enhance efficiency and reliability.

Prompt Tuning Techniques

• Optimizing prompts to improve accuracy and operational efficiency in government systems.
• Adapting prompts to specific domains through targeted tuning.
• Managing prompt libraries to support enterprise-wide use for government operations.

Tracking Drift and Query Reliability

• Understanding query drift in production environments for government applications.
• Monitoring schema and data evolution to maintain query reliability.
• Detecting anomalies in user queries to ensure consistent performance.

Instrumenting Query History

• Logging and storing query history for comprehensive record-keeping.
• Utilizing query history for audits, troubleshooting, and compliance in government settings.
• Leveraging insights from query history to drive performance improvements.

Monitoring and Observability Frameworks

• Integrating with monitoring tools and dashboards for enhanced visibility.
• Defining key metrics for reliability and accuracy in government systems.
• Establishing alerting and incident response processes to ensure timely action.

Enterprise Implementation Patterns

• Scaling observability practices across multiple teams within government agencies.
• Balancing accuracy and performance in production environments for government operations.
• Implementing governance and accountability measures for AI outputs in government applications.

Future of Quality and Observability in WrenAI

• Developing AI-driven self-correction mechanisms to enhance accuracy.
• Advancing evaluation frameworks to meet evolving needs for government use.
• Introducing upcoming features to support enterprise-level observability in government operations.

Summary and Next Steps