Hire Pooja C. - RAG Engineer

Designed and implemented a Retrieval Augmented Generation-powered platform for the travel industry using Langchain and ChromaDB, leading to a 45% increase in user engagement and 2000 active users per month. Reduced customer churn by 21% by designing and deploying an advanced RAG system, effectively surpassing traditional chatbots and competitors by enhancing user interaction capabilities. Leveraged advanced RAG technology to secure investment, driving a 30% increase in company revenue by demonstrating enhanced platform capabilities and future potential. Conducted extensive research and analysis on various RAG technologies, evaluating their uses and limitations to optimize platform performance, resulting in a 20% improvement in system efficiency. Engineered a hybrid RAG system integrating knowledge graphs and vector databases, enhancing data processing and retrieval efficiency, achieving a 25% increase in data retrieval speed. Worked with cross-functional teams to integrate the RAG system into existing travel services, improving user experience and satisfaction, evidenced by a 15% increase in positive user feedback.

Increased information retrieval accuracy by 25% by developing a Question Answering (QA) system using GPT-3 with embeddings and retrieval techniques, focusing on Wikipedia articles about Impact Investing and ESG. Reduced response errors by 30% through the implementation of strategies to prevent hallucination and improve GPT-3's accuracy, including prompt engineering and the addition of explicit contextual information. Enhanced data processing efficiency by 20% by preprocessing a document library into semantically related sections and creating embedding vectors for each section with the OpenAI Embedding API. Boosted question-answering precision by 35% using the OpenAI Completions API to generate responses tailored to user queries with the most relevant contextual information. Magnified GenAI framework's reliability and trustworthiness by 20% through comprehensive data experiments, developing data pipelines compliant with over 10+ international standards and taxonomies. Spearheaded a cross-functional team to achieve a 15% increase in prediction accuracy by enhancing Environmental, Sociological, and Governance (ESG) impact analysis with statistical modeling and machine learning.