Retrieval Augmented Generation (RAG) is an innovative technique in large language models (LLMs) that enhances text generation by integrating real-time data retrieval, resulting in more accurate and contextually relevant outputs. RAG addresses limitations such as hallucinations by allowing models to access external databases during the generation process, which is particularly useful for applications like open-domain question answering and customer support automation. The article explores eight distinct RAG architectures, each tailored for different use cases, including Simple RAG for FAQ systems, Branched RAG for specialized knowledge queries, and more complex forms like Agentic RAG, which autonomously synthesizes information from multiple sources. These architectures offer advantages over other techniques like fine-tuning and prompt engineering by providing dynamic, real-time retrieval capabilities, thus improving the relevance and accuracy of generated responses.