Fine-Tuning vs. RAG vs. Hybrid Approaches for Enterprise Knowledge Tasks: A Systematic Study

Abstract

The use of Large Language Models (LLMs) is growing in enterprises for Knowledge-intensive tasks like searching the technical documentation, verifying compliance regulation, and managing incident responses. But, there remains strategic ambiguity about which of the 'tabs' of fine-tuning approaches, Retrieval-Augmented Generation (RAG) or hybrid architectures (that interweave retrieval and parameter adaptation) should be employed? Comparative studies that already exist are frequently restricted to individual comparisons, using limited datasets and/or a single assessment metric, for example accuracy. As a result, enterprise stakeholders do not have a systematical and operationally informed knowledge of the trade-offs that come from each pathway. In this study, we conduct a thorough comparison among four enterprise knowledge adaptation strategies: parameter-efficient fine-tuning with low-rank adaptation (LoRA), dense retrieval-based RAG systems, mixed retrieval enhanced fine-tuning and parameter-free in-context learning baselines. The study compares these approaches in three enterprise tasks, technical documentation question answering, policy-compliance verification and incident lookup. Experimental data is created from documentation from enterprise-oriented sources like Site Reliability Engineering documents, Open Policy repositories, and technical support documents. This research includes a multi-dimensional evaluation framework, as opposed to previous research that primarily focuses on predictive accuracy, by using the following: deployment cost, tolerance for update frequency, operational complexity, scalability, latency, and maintainability. The study proves that there is no single architecture which is superior to the rest for all enterprise situations. Accurate methods are fine-grained and have a narrow scope for coping with dynamically and rapidly evolving knowledge.