Building Custom Large Language Models for Industries: A Comparative Analysis of Fine-Tuning and Retrieval-Augmented Generation Techniques

In this study, we delve into the development and optimization of custom Large Language Models (LLMs) tailored for specific industrial applications, concentrating on two predominant adaptation techniques: fine-tuning and Retrieval-Augmented Generation (RAG). We employ a rigorous, mathematically-driven approach to compare the efficacy of these methodologies, using a dual dataset derived from TACIT Company, which specializes in the tacit knowledge of retiring workforce documentation, and Sight Machine, which focuses on manufacturing process data. Our evaluation framework uses three metrics: Accuracy, calculated as the ratio of correctly predicted instances to total predictions; Adaptability, which measures the model's performance stability when exposed to novel, evolving datasets; and Industry Relevance, assessed through expert-based qualitative scoring converted to quantitative analysis. This comprehensive comparative analysis not only quantifies the performance differences between finetuning and RAG but also explores the computational dynamics and potential synergies when implementing these models. Additionally, we address the ethical considerations and practical implications of deploying LLMs in sensitive industries, framing these technologies within the context of operational integrity and data governance. The outcome of this research aims to guide stakeholders in selecting and optimizing LLM strategies that align with specific industrial needs, balancing technical efficiency, and ethical standards.