Adapting Large Language Models: A Comparative Study of Prompt Engineering and Fine-Tuning

Large Language Models (LLMs) have become foundational tools in natural language processing but adapting them effectively remains a technical and practical challenge. This paper presents a comparative analysis of prompt engineering and fine-tuning as key strategies for customizing LLM behavior. Drawing on secondary data from studies and technical documents, the study evaluates each method in terms of performance, cost-efficiency, robustness, and use-case suitability. The analysis concludes that such fine-tuning results in a higher task-specific accuracy and stability, especially in domain-intensive uses, but that prompt engineering allows a more flexible over-all task as well as having a reduced resource requirement. LoRA and prefix tuning are parameters-efficient methods that are found as potential trade-offs. It has also been noted in the study that there is a variation in the interpretability and risk exposure between the approaches. The findings serve the purpose of assisting developers and researchers in the recommendation of proper adaptation strategies in accordance with the technical constraints and task requirements