{"id":13221,"date":"2025-04-04T06:29:06","date_gmt":"2025-04-04T14:29:06","guid":{"rendered":"https:\/\/live-cometml.pantheonsite.io\/?page_id=13221"},"modified":"2025-06-12T16:49:39","modified_gmt":"2025-06-12T16:49:39","slug":"jasleen-singh","status":"publish","type":"page","link":"https:\/\/www.comet.com\/site\/convergence\/jasleen-singh\/","title":{"rendered":"Jasleen Singh"},"content":{"rendered":"\n
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\u2190 BACK TO EVENT PAGE<\/a><\/p>\n\n\n\n

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\"Jasleen<\/figure>\n<\/div>\n\n\n\n
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Jasleen Singh<\/h1>\n\n\n

Senior Principal Software Engineer at Dell Technologies<\/h2>\n<\/div>\n\n\n\n
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Jasleen is a Senior Principal Software Engineer at Dell Technologies, with over 10 years of experience in the field of Artificial Intelligence. They hold a Masters degree in Data Science from the University of Texas at Austin and an MBA from Stanford University, combining technical expertise with business strategy to drive impactful AI solutions. Throughout their career, they have successfully transitioned numerous AI projects from proof of concept to scalable products. Their expertise lies in Generative AI implementation across industry-wide products, with a focus on maximizing adoption while minimizing costs. They also have extensive experience in AI POCs, testing, and optimization, with a strong commitment to developing responsible and inclusive AI systems. With 25 patents in AI, they actively share their knowledge through blogs and frequent speaking engagements at AI conferences. Their research interests include AI testing, quantization techniques, and the development of personalized AI systems that deliver meaningful value.<\/p>\n<\/div>\n<\/div>\n<\/div>\n<\/div><\/div>\n\n\n\n

May 13th, 3:05 \u2013 3:35PM ET<\/p>\n\n\n\n

From Hallucination to Accuracy: Evaluating Context Retrieval in RAG Systems<\/h2>\n\n\n\n
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In Retrieval-Augmented Generation (RAG) systems, the quality of retrieved context is critical to ensuring accurate and reliable outputs from Large Language Models (LLMs). Poor-quality context leads to \u201cgarbage in, garbage out\u201d issues, resulting in hallucinated or misleading responses. Manual evaluation of context retrieval is both time-intensive and costly, often requiring domain experts.<\/p>\n\n\n\n

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