Introduction to LLM Customization

Recent advancements in artificial intelligence have led to the development of large language models (LLMs), revolutionizing natural language processing. These powerful models, such as ChatGPT and Llama, demonstrate superior capabilities in understanding and generating human-like language but are limited by their training data cut-off date. To unlock their full potential, LLM customization is essential. Customization options include Retrieval Augmented Generation (RAG) and fine-tuning methods like supervised fine-tuning and Reinforcement Learning from Human Feedback (RLHF). RAG enhances response quality by injecting relevant contexts alongside the query, while fine-tuning involves training LLMs on specific data domains.