LEVERAGING LARGE LANGUAGE MODELS (LLM) FOR IMPROVING ORGANIZATIONAL EFFICIENCY
The primary objective is to reduce the manual labor of organizations by creating a framework that automises the processing of unstructured data to create useful data insights.
Abstract
Large Language Models (LLMs) are a rising topic in all industries across the world with large potential. This thesis will focus on investigating the use of LLMs to improve the efficiency/productivity of stakeholders (benefiting organisations themselves), specifically automating the process of generating personalised data insights that would be used for important decision-making. Increasing amounts of data over the past years have created a need for tool creation which aids the pace at which organizations operate and make impactful decisions. This study also covers the conversion of unstructured data to create useful data insights specific to different stakeholders. The primary objective is to reduce the manual labor of organizations by creating a framework that automises the processing of unstructured data to create useful data insights. The research highlights a significant reduction in data processing time when transferred from manual to automated, as well as showing the potential of the LLMs being applied to a variety of real-life problems.