The article delves into Retrieval-Augmented Generation (RAG), which integrates retrieval and generative models to enhance GenAI applications efficiently. It highlights the architecture of RAG, utilizing vector databases for data retrieval and response generation.
This article explores the evolution from Transformers to Large Language Models (LLMs), detailing the mechanisms of self-attention and multi-head attention, the role of position embeddings, various types of transformer models, and the training and fine-tuning processes of LLMs.
