# LLM-Powered Data Classification at Grab ## Overview and Context Grab, a leading Southeast Asian super-app platform, implemented an LLM-powered system to automate data classification across their PetaByte-scale data infrastructure. The project emerged from a critical need to efficiently manage and protect sensitive data while improving data discovery processes. The system, named Gemini, represents a sophisticated implementation of LLMOps principles in a production environment. ## Technical Implementation ### System Architecture - Orchestration Service (Gemini) ### LLM Integration Considerations - Context Length Management - Resource Management ### Prompt Engineering Strategy - Carefully crafted prompts for data classification tasks - Key techniques implemented: - JSON output format enforcement for downstream processing ### Production Pipeline Components - Data Platforms - Message Queue System - Verification System ## Operational Aspects ### Performance Metrics - Processing Capacity - Accuracy Measurements ### Cost Optimization - Contrary to common assumptions, the system proved highly cost-effective - Scalable solution capable of handling increased data entity coverage ### Production Safeguards - Rate limiting implementation - Token quota management - Error handling mechanisms - Batch processing optimization ## Governance and Compliance ### Data Security Measures - Personal Identifiable Information (PII) detection - Sensitivity tier classification - Integration with Attribute-based Access Control (ABAC) - Dynamic Data Masking implementation ### Verification Workflow - Regular user verification cycles - Feedback collection system - Iterative prompt improvement process - Compliance with regulatory requirements ## Future Developments ### Planned Enhancements - Sample data integration for improved accuracy - Confidence level output implementation - Automated verification threshold system - Analytical pipeline for prompt performance tracking ### Scaling Initiatives - Extension to additional data platforms - Development of downstream applications - Security and data discovery integrations ## Technical Impact and Benefits ### Efficiency Gains - 360 man-days saved annually - Reduced manual classification effort - Improved data governance processes - Enhanced data discovery capabilities ### System Reliability - High classification accuracy - Minimal human verification needed - Robust error handling - Scalable architecture ## Implementation Learnings ### Best Practices - Clear prompt engineering principles - Effective rate limiting strategies - Robust verification workflows - Cost-effective scaling approaches ### Technical Challenges Addressed - Context length limitations - Token quota management - Output format standardization - Integration with existing systems ## Production Monitoring ### Key Metrics - Processing volume tracking - Accuracy measurements - Cost monitoring - User feedback collection ### Quality Assurance - Regular verification cycles - Prompt performance tracking - User feedback integration - Continuous improvement process The implementation demonstrates a sophisticated approach to LLMOps in a production environment, successfully balancing automation, accuracy, and cost-effectiveness while maintaining robust governance standards. The system's success in handling large-scale data classification while reducing manual effort showcases the practical benefits of well-implemented LLM solutions in enterprise environments.