Retrieval Augmented Large Language Models for Intelligent Information Systems

Abstract

Large Language Models have revolutionised intelligent information systems by offering human-like reasoning, contextual comprehension, and natural language creation capabilities to a range of applications. But traditional LLMs suffer from major limitations: stale knowledge, delusion, limited domain competence, and inability to acquire real-time information. Retrieval-Augmented Generation alleviates these problems by combining external knowledge retrieval with generative language models, such that generated answers are founded in trustworthy and regularly updated knowledge sources. In this research, we describe a Retrieval-Augmented Large Language Model framework for intelligent information systems that integrates semantic document retrieval, vector embeddings, knowledge indexing and context-aware response generation into a single architecture. The architecture also offers feedback-driven knowledge refining for ongoing enhancement We implement the suggested system to improve decision support, reduce hallucination, and improve the accuracy of retrieval in many application areas such as healthcare, education, finance, legal services, cybersecurity, corporate knowledge management, and scientific research. Experiments show that the retrieval-augmented LLMs significantly outperforms solo LLM-based systems in terms of contextual relevance, factual correctness, response reliability, and scalability. The suggested framework provides a viable and adaptable method for development of next generation intelligent information sys-tems that can provide trustworthy, real-time and domain-aware knowledge support.