Large Language Models (LLMs) like ChatGPT often face limitations in enterprise applications due to their training on public datasets and finite context windows, making them less effective with proprietary data. To address these challenges, two primary solutions are explored: Fine-Tuning and Retrieval-Augmented Generation (RAG). Fine-tuning involves training the LLM with domain-specific data to generate accurate responses, offering benefits like customized outputs and offline operation but at the cost of high resource demands and static knowledge. Conversely, RAG enhances LLM capabilities by supplementing queries with relevant data from external databases, allowing for dynamic updates and scalability, though it still faces context window limitations and requires integration efforts. Each method has distinct advantages, with fine-tuning suiting static, repetitive tasks and RAG excelling in dynamic, real-time applications. In some cases, combining both approaches can optimize LLM performance by blending domain-specific understanding with real-time data access, providing a comprehensive solution tailored to enterprise needs.