Recurrent Neural Networks (RNNs) are a class of artificial neural networks designed for processing sequential data. They excel in tasks where context and order are crucial, such as language modeling and time series prediction. RNNs maintain a hidden state that captures information from previous inputs, allowing them to generate outputs based on both current and past data. Variations like autoregressive models and sequence-to-sequence architectures enhance their capabilities, enabling them to handle complex tasks like translation and text generation. Understanding RNNs is essential for grasping more advanced models, including Transformers, which build upon the foundational concepts of RNNs.