@Zeroshot Our model leverages the Retrieval Augmented Generation (RAG) approach by combining pre-trained retrieval models with generative models to enhance the quality and relevance of generated responses. Here's an example to illustrate how this works: Let's say a user asks our model a question like, 'What are the symptoms of COVID-19?' Our model first uses a retrieval component to search through vast amounts of pre-existing knowledge sources, such as articles, websites, and databases, to find relevant information related to COVID-19 symptoms. The retrieval component identifies key passages or documents that are most likely to contain accurate and up-to-date information on the topic. Once the relevant information has been retrieved, the generative component of our model synthesizes this information and generates a comprehensive response that answers the user's question. The generative model ensures that the response is coherent, well-organized, and tailored to the user's query, incorporating both the retrieved facts and contextual understanding to provide a detailed explanation of COVID-19 symptoms. By integrating retrieval and generation processes in this way, our model can deliver more accurate, informative, and contextually relevant responses to user queries, enhancing the overall user experience and increasing the reliability of the information provided.