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DTSTAMP:20250531T135205Z
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DTSTART;TZID=America/Chicago:20250528T110000
DTEND;TZID=America/Chicago:20250528T120000
DESCRIPTION:This session will delve into the dynamic and rapidly advancing 
 field of Retrieval Augmented Generation (RAG)\, an innovative approach tha
 t merges traditional information retrieval techniques with generative AI m
 odels to produce content that is more accurate\, relevant\, and grounded i
 n factual data. It will begin with an overview of the fundamental paradigm
 s of RAG\, emphasizing its distinction from purely generative or purely re
 trieval-based systems. The session will then explore the key technologies 
 that power RAG\, including vector databases\, dense retrievers such as Chr
 omaDB and FAISS\, and advanced transformer-based language models like BERT
  and GPT. Attendees will gain insights into RAG pipeline architectures\, i
 ntegration complexities\, and deployment strategies in real-world environm
 ents. The session will conclude with a discussion of emerging trends and r
 esearch directions\, such as domain-adapted RAG\, low-latency retrieval\, 
 and scalable knowledge grounding\, offering a comprehensive understanding 
 of both the conceptual foundations and practical applications of RAG in to
 day's AI landscape.\n\nSpeaker(s): Dr. Rajanikanth Aluvalu\n\nVirtual: htt
 ps://events.vtools.ieee.org/m/486210
LOCATION:Virtual: https://events.vtools.ieee.org/m/486210
ORGANIZER:manishaguduri@ieee.org
SEQUENCE:32
SUMMARY:IEEE Lafayette Section : Summer Digital Dialogues - Retrieval Augme
 nted Generation(RAG): Paradigms\, Technologies and Trends
URL;VALUE=URI:https://events.vtools.ieee.org/m/486210
X-ALT-DESC:Description: <br /><p>This session will delve into the dynamic a
 nd rapidly advancing field of Retrieval Augmented Generation (RAG)\, an in
 novative approach that merges traditional information retrieval techniques
  with generative AI models to produce content that is more accurate\, rele
 vant\, and grounded in factual data. It will begin with an overview of the
  fundamental paradigms of RAG\, emphasizing its distinction from purely ge
 nerative or purely retrieval-based systems. The session will then explore 
 the key technologies that power RAG\, including vector databases\, dense r
 etrievers such as ChromaDB and FAISS\, and advanced transformer-based lang
 uage models like BERT and GPT. Attendees will gain insights into RAG pipel
 ine architectures\, integration complexities\, and deployment strategies i
 n real-world environments. The session will conclude with a discussion of 
 emerging trends and research directions\, such as domain-adapted RAG\, low
 -latency retrieval\, and scalable knowledge grounding\, offering a compreh
 ensive understanding of both the conceptual foundations and practical appl
 ications of RAG in today's AI landscape.</p>
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