Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Conference Paper Faculty Publications- Conference Papers Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Sudhir, Pratul; Suresh, Sreya; Thontadari, C. Title A name given to the resource Leveraging Agentic RAG toReduce Hallucinations inLarge Language Models Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2025 Source A related resource from which the described resource is derived Lecture Notes in Networks and Systems;Volume;1355 LNNS;pp.31-42 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1007/978-981-96-4883-2_4" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/978-981-96-4883-2_4</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105010631290?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105010631290?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Sudhir P., Department of Computer Science, Christ University, Bangalore, India; Suresh S., Department of Computer Science, Christ University, Bangalore, India; Thontadari C., Department of Computer Science, Christ University, Bangalore, India Description An account of the resource Large Language Models (LLMs) have revolutionized language generation and comprehension. However, a notable issue remains, which is their sensitivity to hallucination, which may lead them to generate inaccurate or irrelevant content. Context dependency, or the capacity to use and understand ones environment, is crucial for surviving hallucinations. The agentic RAG framework offers a feasible solution, leveraging intelligent agents to strengthen contextual knowledge. Through the evaluation of entity roles and relationships, agentic RAG aids LLMs in understanding the variation of context, spotting inconsistencies and generating more precise and balanced replies. This research explores the establishment of Agentic RAG into LLMs to refine their reliability and efficiency by overcasting hallucinations and lifting contextual awareness. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. Subject The topic of the resource Large language models; LLM Hallucinations; RAG Systems Publisher An entity responsible for making the resource available Springer Science and Business Media Deutschland GmbH Relation A related resource ISSN: 23673370; ISBN: 978-981964882-5; Language A language of the resource English Type The nature or genre of the resource Conference paper Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Format The file format, physical medium, or dimensions of the resource online