OPTIMIZING DOMAIN-SPECIFIC LARGE LANGUAGE MODELS: A COMPARATIVE ANALYSIS OF RETRIEVAL-AUGMENTED GENERATION (RAG) AND FINE-TUNING METHODOLOGIES - Geet, Govind; Ankit, Agarwal; D, Rajesh (ShodhKosh: Journal of Visual and Performing Arts, 2026)
Large Language Models (LLMs) demonstrate substantial general-world knowledge derived from large-scale pretraining corpora. However, their utility in enterprise environments is constrained by static training data, temporal knowledge cut-offs, and limited access to proprietary or real-time information. Two principal methodologies have emerged to address these constraints: Retrieval-Augmented Generation (RAG) and Fine-Tuning. This paper provides a technical examination of both paradigms, analysing their architectures, operational trade-offs, cost profiles, and failure modes. It concludes by advocating for a hybrid framework—Retrieval-Augmented Fine-Tuning (RAFT)—as a robust strategy for domain-specialized enterprise deployments.