OPTIMIZING DOMAIN-SPECIFIC LARGE LANGUAGE MODELS: A COMPARATIVE ANALYSIS OF RETRIEVAL-AUGMENTED GENERATION (RAG) AND FINE-TUNING METHODOLOGIES
DOI:
https://doi.org/10.29121/shodhkosh.v7.i7s.2026.7928Keywords:
LLMS , Rag, Fine-Tuning, Raft, Enterprise AI, Knowledge Limits, Real-Time Data, Domain SpecializationAbstract [English]
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.
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Copyright (c) 2026 Govind Geet, Agarwal Ankit, Dr. Rajesh D.
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