In the field of RAG development, Retrieval-Augmented Generation (RAG) tackles a shortcoming of large language models (LLMs) by incorporating real-world knowledge. LLMs are great at using language, but lack access to specific details. RAG bridges this gap by combining retrieval and generation models. The retrieval model finds relevant information from a vast external source, like Wikipedia, while the generation model uses its language skills to craft a response using both the retrieved info and its own knowledge. This lets RAG provide accurate and up-to-date responses in tasks like question answering and chatbots.