This case study explores Hostinger's systematic approach to implementing LLM evaluation in production, showcasing a practical framework for ensuring chatbot quality and continuous improvement. The presentation, delivered by a member of Hostinger's AI team, provides valuable insights into how a tech company approaches the challenges of maintaining and improving LLM-based systems in production. ## Overview of the Evaluation Framework Hostinger's approach to LLM evaluation is built around three primary goals: * Offline evaluation of changes during development using predefined datasets * Continuous assessment of live chatbot performance * Identification and prioritization of emerging issues for iterative improvement The framework represents a holistic approach to LLM evaluation that bridges the gap between development and production environments. This is particularly noteworthy as it addresses one of the key challenges in LLMOps: ensuring consistency between testing and real-world performance. ## Technical Implementation The evaluation framework consists of several key components working together: ### Development Pipeline The development process begins with proposed improvements that undergo evaluation against "golden examples" - carefully curated examples of ideal chatbot responses. This approach demonstrates a commitment to maintaining high standards of output quality while allowing for iterative improvement. The system uses specific metrics to measure how closely the actual responses match these ideal examples. ### Production Monitoring In production, the framework continuously evaluates real customer interactions, providing insights into actual performance and identifying areas for improvement. This creates a feedback loop that informs future development priorities. ### Evaluation Components The framework implements multiple layers of evaluation: * Basic component checks for output correctness and URL validation * Sophisticated metrics for measuring factuality and directional accuracy * Comparison mechanisms between ideal and actual responses * Integration of human evaluation for quality assurance * User feedback collection from the frontend ### BrainTrust Integration A significant aspect of the implementation is the integration with BrainTrust, a third-party tool that helps automate the evaluation workflow. This integration demonstrates a practical approach to scaling evaluation processes while maintaining consistency and reliability. The system allows for: * Programmatic execution of evaluations * Automatic comparisons between experiments * Detailed drilling down into specific issues * Management of evaluation datasets * Collection and organization of human feedback ## Technical Infrastructure The implementation is notably lightweight and flexible, with key features including: * Integration with GitHub Actions for automated evaluation on code changes * Programmatic API access for running evaluations * Support for multiple teams and services * Customizable metrics and evaluation criteria * UI-based dataset management and result visualization ## Dataset Management The system includes sophisticated handling of evaluation datasets: * Programmatic addition of new examples * UI-based editing and management of golden examples * Organic growth of evaluation datasets through team contributions * Continuous refinement of evaluation criteria ## Challenges and Solutions While the presentation highlights the positive aspects of the framework, it's important to note several challenges that such systems typically face: * Maintaining consistency in evaluation criteria across different teams * Balancing automated metrics with human evaluation * Ensuring the scalability of the evaluation process * Managing the growth and quality of golden examples * Handling edge cases and unexpected inputs ## Results and Impact The framework appears to have several positive outcomes: * Streamlined evaluation process for multiple teams * Improved visibility into chatbot performance * Better tracking of improvements and regressions * More systematic approach to quality assurance * Enhanced collaboration between different stakeholders ## Future Directions The presentation indicates that this is an evolving framework, with plans for: * Expanding usage across multiple teams * Adding more sophisticated evaluation metrics * Enhancing the golden example database * Improving automation capabilities * Refining the feedback loop between production and development ## Critical Analysis While the framework appears well-designed, there are some considerations worth noting: * The heavy reliance on golden examples might not catch all edge cases * The balance between automated and human evaluation needs careful management * The scalability of the approach with growing usage needs to be monitored * The effectiveness of the metrics in capturing real user satisfaction needs validation ## Best Practices Demonstrated The case study highlights several LLMOps best practices: * Systematic approach to evaluation * Integration of multiple feedback sources * Automation of routine tasks * Clear metrics and evaluation criteria * Continuous monitoring and improvement * Cross-team collaboration support * Version control and experiment tracking This case study provides valuable insights into how companies can approach LLM evaluation in a systematic way, balancing automated metrics with human insight while maintaining a focus on continuous improvement. The framework's flexibility and scalability make it a noteworthy example of practical LLMOps implementation.