Library

OPTIMIZING DOMAIN-SPECIFIC LARGE LANGUAGE MODELS: A COMPARATIVE ANALYSIS OF RETRIEVAL-AUGMENTED GENERATION (RAG) AND FINE-TUNING METHODOLOGIES

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.

Security & robustnessrobustness
Source

Paper

Full text

PDF

OPTIMIZING DOMAIN-SPECIFIC LARGE LANGUAGE MODELS: A COMPARATIVE ANALYSIS OF RETRIEVAL-AUGMENTED GENERATION (RAG) AND FINE-TUNING METHODOLOGIES

Semantic Scholar · 2026

Abstract

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.

Source of record · Semantic Scholar

Similar papers

Fine-Tuning Pre-Trained Language Models for Improved Retrieval in RAG Systems for Domain-Specific UseSyed Arham Akheel -2024 · International Journal For Multidisciplinary Research In Library
Building Custom Large Language Models for Industries: A Comparative Analysis of Fine-Tuning and Retrieval-Augmented Generation TechniquesVarsha Shukla, Geoffrey G. Parker2024 In Library
Search Enhancement Generation for Fine-Tuning Large Language ModelsYonghao Xing, Shuxin Du, Yi Qiu et al.2024 In Library
Unveiling the Power of Large Language Models: A Comparative Study of Retrieval-Augmented Generation, Fine-Tuning, and Their Synergistic Fusion for Enhanced PerformanceGülsüm Budakoglu, Hakan Emekci2025 · IEEE Access · 19 citations In Library
Augmenting Large Language Models with External Data Sources: A Systematic Review of Methodologies, Performance Metrics, and Information FidelitySoham Mukherjee, John D. Le, Chau Nguyen et al.2026 · Preprints.org In Library
Augmenting Large Language Models with External Data Sources: A Systematic Review of Methodologies, Performance Metrics, and Information FidelitySoham Mukherjee, John D. Le, Chau Nguyen2026 · Knowledge In Library
When Do Large Language Models Need Retrieval? A Comparative Study of RAG, Fine-Tuning, and Hybrid Adaptation StrategiesAit El Abbas Ilias2026 · International Journal of Research and Innovation in Social Science In Library
Retrieval-Augmented Generation (RAG) and Large Language Models (LLMs) for Enterprise Knowledge Management and Document Automation: A Systematic Literature ReviewEhlullah Karakurt, A. Akbulut2025 · Applied Sciences · 6 citations In Library
Retrieval-Augmented Generation With Large Language ModelsDena Abu Laila, Qais Al-Na'amneh, Mahmoud Aljawarneh et al.2025 In Library
Two-Stage Fine-Tuning for Retrieval-Augmented Large Language Models via Retriever and Prompt OptimizationXiaochen Lai, Xiaodi Pan, Yongkang Su et al.2025 In Library
© 2026 NYSGPT2525 LLC