Authors : Utsha Sarker; Archy Biswas; Ikram Ali; Lalit Vaishnav; Harsh; Priyanshu Agarwal

Volume/Issue : Volume 11 - 2026, Issue 3 - March

Google Scholar : https://tinyurl.com/2nddcuau

Scribd : https://tinyurl.com/yvx8scma

DOI : https://doi.org/10.38124/ijisrt/26mar1756

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

Abstract : Large Language Models (LLMs) have been shown to have remarkable capabilities in natural language understanding; however, they still have some limitations such as the outdated knowledge, the lack of domain-specific awareness and the hallucination of incorrect information. These problems are induced by the fact that LLMs are mainly based on parametric knowledge stored during the training process, that is not dynamically updated and verified . To combat such challenges, this paper introduces an improved Retrieval-Augmented Generation (RAG) to address these underlying challenges which combines an improved context aware retrieval mechanism with the gating based prompt augmentation strategy. The proposed approach selectively filters and ranks the retrieved documents based on context-awareness gate before injecting them to the LLM, which would improve the relevance and reduce the noise in the generated responses.In the paper we validate the proposed method using benchmark data such as SQuAD, domain-specific question answering data sets as well as dialogue data sets where we compare with baseline models such as vanilla LLMs and standard RAG pipelines. Experimental results show that our method can provide much better results in terms of Exact Match (EM), F1-score and Fact consistency compared to traditional methods. These findings are consistent with recent studies showing the value of RAG in enhancing factual grounding and reducing hallucinations in LLMs 1.  Contributions: In this paper, we propose a novel context-aware RAG architecture, which provides a retrieval filtering mechanism. Following the review, we design an improved prompt integration strategy for improved knowledge grounding. We empirically show better performance on several NLP benchmarks.

Keywords : Retrieval Augmented Generation (RAG), Large Language Model (LLMs), Context Aware Natural Language Understanding, Contextual Retrieval, Knowledge Intensive NLP.

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