Retrieval-Augmented Generation

LLMs are highly capable of processing vast amounts of information and developing a strong general understanding of it. Their training on large and diverse datasets enables them to answer a wide range of questions across many topics. However, this knowledge is limited to what is contained in their training data. This means they may struggle to answer certain questions about unfamiliar topics or produce responses that do not align with a user’s specific goals. This limitation is expected since the model’s knowledge and biases are shaped by its training data.