ICA-RAG: An Intelligent Context-Aware Conversational System Using Retrieval-Augmented Generation | IJCT Volume 13 – Issue 3 | IJCT-V13I3P94
International Journal of Computer Techniques
ISSN 2394-2231
Author
DAKA VENKATA PRANEETH REDDY, BODDAPATI POORNA CHANDRA CHOWDARY, BYLADUGU RAVI TEJA
Abstract
In the modern digital era, the exponential growth of data across educational, industrial, and organizational domains has necessitated the development of intelligent systems capable of efficiently retrieving, processing, and presenting information. Traditional
information retrieval systems, primarily based on keyword matching, fail to capture the semantic intent behind user queries, leading to suboptimal results. Recent
advancements in Artificial Intelligence, particularly Large Language Models (LLMs), have demonstrated remarkable capabilities in natural language understanding and
generation. However, these models are inherently limited by static knowledge, susceptibility to hallucination, and lack of real-time adaptability.
This paper proposes ICA-RAG, an Intelligent Context-Aware Conversational System that integrates Retrieval-Augmented Generation (RAG) with Large Language Models to overcome these limitations. The system retrieves relevant documents from external knowledge bases and utilizes LLMs to generate accurate, context-aware, and human-like responses. The proposed architecture enhances factual accuracy, reduces
misinformation, and supports dynamic knowledge integration. Comprehensive analysis, system design, methodology, and evaluation results demonstrate that ICA-RAG
significantly outperforms traditional chatbots and standalone LLM-based systems in terms of accuracy, efficiency, and user satisfaction.
Keywords
Artificial Intelligence, Retrieval-Augmented Generation, Large Language Models, Conversational AI, Information Retrieval, Natural Language Processing, Chatbots, Knowledge Systems
Conclusion
The ICA-RAG system presents an advanced approach to conversational AI by integrating retrieval and generation techniques. It successfully addresses the limitations of traditional information retrieval systems and standalone LLMs.
By providing accurate, context-aware, and real-time responses, the system enhances user experience and opens new possibilities in various domains. The proposed architecture is scalable, efficient, and adaptable, making it suitable for future advancements in AI-driven communication systems.
References
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How to Cite This Paper
DAKA VENKATA PRANEETH REDDY, BODDAPATI POORNA CHANDRA CHOWDARY, BYLADUGU RAVI TEJA (2026). ICA-RAG: An Intelligent Context-Aware Conversational System Using Retrieval-Augmented Generation. International Journal of Computer Techniques, 13(3). ISSN: 2394-2231.
ICA-RAG An Intelligent Context-Aware Conversational System Using Retrieval-Augmented GenerationDownload
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