# Using LLMs to Test LLMs: Alaska Airlines' QARL Testing Framework ## Overview Alaska Airlines, in partnership with Bitra (a Google Cloud services partner), developed an innovative approach to testing LLM-based chatbots in production environments. Their solution, QARL (Quality Assurance Response Liaison), uses LLMs to test other LLMs by simulating various user interactions and evaluating responses for safety, appropriateness, and business alignment. ## Background and Motivation - Alaska Airlines developed a customer-facing travel chatbot for helping users find ideal travel destinations - The team identified significant risks with deploying LLMs in production: - Previous incidents in the industry highlighted these risks: ## Technical Architecture ### Core Components - Model Garden - Conversation Coordinator - Storage & Analytics ### Testing Framework Features - Customizable Test Parameters - Flexible Integration Options ## Implementation Details ### Test Case Design - Uses loose human prompts to define test scenarios - Supports multiple persona types: - Customizable conversation depth to manage API costs and rate limits ### Scoring and Evaluation - Automated scoring system (1-10 scale) - Evaluation criteria based on: ### Operational Aspects - CI/CD Integration - Human-in-the-Loop Components - Monitoring and Maintenance ## Results and Benefits ### Key Improvements - Higher customer satisfaction scores post-QARL implementation - Reduced risk of inappropriate bot behaviors - Faster testing and deployment cycles - More comprehensive coverage of edge cases ### Operational Efficiency - Small team requirement (started with single data scientist) - Scalable testing framework - Automated identification of problematic patterns - Efficient handling of multiple LLM variants ## Future Developments - Planned Enhancements ## Best Practices and Lessons Learned - System Design Insights - Testing Strategy - Implementation Approach