Lessons Learned: Fine-Tuning a Generative AI Model for Internal Knowledge Management - Pitfalls and Successes

Abstract

This article examines the implementation journey of fine-tuning a Large Language Model (LLM) for internal knowledge management within an enterprise environment. It explores the challenges and successes encountered during the deployment of an AI-driven system designed to enhance information retrieval and knowledge sharing across organizational departments. The article also addresses critical aspects of data security and access control implementation, emphasizing the importance of robust security frameworks in protecting sensitive corporate information. Furthermore, it discusses the maintenance and evolution strategies necessary for ensuring long-term system effectiveness, including continuous learning approaches and automated validation pipelines. Through comprehensive analysis, this article provides valuable insights for organizations considering similar AI-driven knowledge management initiatives.