Context-Aware Retrieval-Augmented Large Language Model Framework For Domain-Specific Knowledge Reasoning

Abstract

Large Language Models (LLMs) have been highly successful in natural language understanding and generation, but their performance on domain-specific tasks is often constrained by stale knowledge, a lack of contextual awareness, and producing responses that are not supported or hallucinated. Retrieval-Augmented Generation (RAG) overcomes some of these shortcomings by using external knowledge in inference, but standard RAG models use semantic similarity and often neglect so-called contextual relationships that are fundamental to accurate domain specific thinking. To address these issues, in this paper, a Context-Aware Retrieval-Augmented Large Language Model Framework of domain-specific knowledge reasoning is proposed. The suggested framework combines the concept of vector-based retrieval, context modeling, and the reasoning of the LLM to retrieve the most relevant data and generate context-specific responses. A context-aware retrieval system also filters retrieved documents prior to the supply to the LLM to enhance the quality of reasoning and its reliability. Experimentally, the framework is compared with standalone LLMs and traditional methods of RAG based on retrieval precision, answer accuracy, explainability score, hallucination rate, and response latency as measures of performance. As illustrated by the experimental findings, the proposed framework is very effective in enhancing retrieval precision, answer accuracy and minimising the hallucinated responses and low response latency. The suggested method improves the clarity, robustness, and proficiency of domain-specific knowledge reasoning by offering an operable model of intelligent applications in customized domain in need of precise and clarifiable AI-aided decision production.