Edmunds, a prominent online car shopping platform, implemented a sophisticated GenAI solution to revolutionize their dealer review moderation process. This case study demonstrates a practical application of LLMs in production, highlighting both the technical implementation details and the organizational challenges faced during deployment. ## Initial Challenge and Context Edmunds faced a significant operational bottleneck in moderating dealer quality-of-service reviews. The platform receives over 300 daily reviews that require careful assessment before publication. The manual moderation process was resource-intensive, requiring up to 72 hours for review publication and consuming substantial staff time. ## Technical Implementation Journey The implementation journey was particularly interesting from an LLMOps perspective, as it showcases the iterative nature of deploying LLMs in production: ### Initial Attempts and Challenges * The team first attempted to use and fine-tune an off-the-shelf model, which proved unsuccessful due to the complexity of moderation rules * Early experiments with basic prompt engineering showed limitations in handling edge cases * The team needed a way to systematically compare different model outputs ### Final Solution Architecture The successful implementation leveraged several key components: * GPT-4 as the core language model * Databricks Model Serving for model deployment and management * Custom prompt engineering optimized for review moderation * Integration with Unity Catalog for data governance ### Prompt Engineering Approach The team developed sophisticated custom prompts that could: * Accurately identify dealer quality-of-service reviews * Make binary accept/reject decisions * Handle complex moderation rules * Process reviews in seconds rather than hours ## Data Governance and Infrastructure A crucial aspect of the implementation was solving data governance challenges. The team migrated to Databricks Unity Catalog, which provided several key benefits: * Fine-grained access control replacing coarse IAM roles * Improved pipeline dependency tracking * Better data lineage documentation * Simplified metadata synchronization through external tables * Account-level metastore management ## Production Deployment Considerations The deployment strategy showed careful consideration of several LLMOps best practices: * Gradual migration approach allowing teams to transition at their own pace * Integration of model serving with existing data infrastructure * Implementation of proper access controls and rate limiting * Careful monitoring of model outputs and performance ## Performance and Results The production deployment achieved significant improvements: * Review moderation time reduced from 72 hours to minutes * Staff time savings of 3-5 hours per week * Reduction in required moderators from a larger team to just two people * Improved consistency in moderation decisions * Better auditing and compliance capabilities * Reduced operational overhead ## Technical Infrastructure Details The solution leverages several key technical components: * AWS as the cloud infrastructure * Databricks Data Intelligence Platform for ML operations * Unity Catalog for data governance * Model Serving endpoints for LLM deployment * External table synchronization for metadata management ## Lessons Learned and Best Practices The implementation revealed several important insights for LLMOps: * The importance of thorough prompt engineering over model fine-tuning for certain use cases * The value of having a unified platform for both data warehousing and AI/ML operations * The benefits of implementing proper data governance from the start * The advantage of being able to easily compare different models' performance * The importance of maintaining flexibility for edge cases ## Future Directions Edmunds is planning to expand their AI-driven approach across all review types, indicating confidence in their implementation. They view their current success as a blueprint for further AI integration, with particular emphasis on: * Expanding to other types of reviews * Further integration of AI into their data pipeline * Continued enhancement of their prompt engineering approach * Potential exploration of new model capabilities The case study demonstrates how careful attention to LLMOps practices - including prompt engineering, model serving infrastructure, data governance, and gradual deployment - can lead to successful production implementation of LLM-based solutions. It also highlights the importance of choosing the right technical stack and implementation approach based on specific use case requirements rather than simply applying the latest trending solutions.