# OLX Job Role Extraction Case Study: LLMOps Implementation ## Project Overview OLX, a major online classifieds platform, implemented a sophisticated LLM-based system to automate job role extraction from their job listings. The project leverages Prosus AI Assistant, a proprietary LLM solution, to enhance their job search functionality by creating standardized job role taxonomies and automating role extraction from job descriptions. ## Technical Implementation ### LLM Selection and Infrastructure - Chose Prosus AI Assistant over self-hosted LLMs due to: ### Production Pipeline Architecture - Built a comprehensive job-role extraction pipeline including: - Processing volume: ### Data Processing and Preparation - Implemented careful data sampling: - Data preprocessing steps: - Search keyword analysis: ## LLM Operations Details ### Prompt Engineering - Developed specific prompt engineering practices: - Used LangChain framework for: ### Taxonomy Generation Process - Created hierarchical job-role taxonomy: - Example prompt structure: ```plain text ### Task Description ### Consider the following top-searched keywords and job-roles in the {category} category... ### Expected Output Format ### ``` ### Production System Integration - Implemented two-phase deployment: - Created subscription service for ad events - Integrated with search indexing system - Built monitoring and validation systems ## Monitoring and Evaluation ### Performance Metrics - Conducted A/B testing focusing on: - Segmented results analysis: ### Quality Control - Informal accuracy monitoring during annotation - Initial validation with 100 sample extractions - Continuous monitoring of extraction quality - Regular taxonomy updates based on category changes ## Challenges and Solutions ### Category Evolution Management - Developed systems to handle dynamic category changes: ### Resource Optimization - Managed API request volume through: ### Cost Management - Current operational costs: ## Future Developments ### Planned Improvements - Considering transition to self-hosted models - Exploring broader information extraction - Enhanced search relevance integration - Automated taxonomy update system ### Scalability Considerations - Planning for increased processing volumes - Infrastructure optimization - Cost reduction strategies - Enhanced monitoring systems ## Lessons Learned and Best Practices ### Implementation Insights - Importance of thorough prompt engineering - Value of iterative testing and refinement - Need for balanced resource utilization - Significance of proper monitoring and evaluation ### Technical Recommendations - Start with managed solutions for quick deployment - Plan for potential self-hosting transition - Implement robust monitoring from the start - Focus on prompt optimization and management - Consider long-term cost implications This case study demonstrates a successful implementation of LLMOps in a production environment, highlighting both the immediate benefits and long-term considerations of using LLMs for business-critical tasks. The project showcases the importance of careful planning, monitoring, and cost management in LLM-based solutions.