Navigating the Evolution of Large Language Models in Business Analysis: A Comparative Study of RAG, Prompt Engineering, and Fine-Tuning Techniques
Navigating the Evolution of Large Language Models in Business Analysis: A Comparative Study of RAG, Prompt Engineering, and Fine-Tuning Techniques

Author(s): Andrea Alberici, Nevila Baci
Subject(s): Social Sciences
Published by: Udruženje ekonomista i menadžera Balkana
Keywords: Large Language Models; Business analysis; Domain-specific languages; Retrieval-augmented generation; Prompt engineering; Fine-tuning; Intentional frameworks
Summary/Abstract: The rapid advancements in large language models (LLMs) could prove to have significantly impacted the field of business analysis, particularly in the development of domain-specific languages (DSLs) tailored to describe business requirements with precision and flexibility. The study highlights the substantial progress in LLM capabilities, including extended context understanding, enhanced reasoning, and mathematical functionalities, which collectively facilitate deeper integration of domain-specific knowledge into business analysis processes. The authors critically assess the relevance of Retrieval Augmented Generative techniques that offer advanced knowledge injection methods, along with prompt engineering reasoning techniques, as opposed to fine-tuning LLMs. Furthermore, the research evaluates the strategic decision-making process for business analysts in adopting these technological advancements. The paper discusses whether business analysts should take a proactive or cautious approach when incorporating these AI-driven methodologies into their analytical frameworks, or just wait for the next turn of LLM’s improvements. By examining various case studies and conducting interviews with experts, this study provides insights into how the deliberate application of advanced LLM techniques can offset the services brought by RAG/Prompt engineering techniques. The text also provides guidance for navigating the technological landscape, indicating that it is important to stay updated with rapid advancements. A strategic combination of RAG, prompt engineering, and fine-tuning can provide a balanced and effective approach to creating intentional frameworks that meet the evolving needs of businesses today.

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