You are developing a chatbot that needs to understand and generate responses based on user input. Which characteristic of Recurrent Neural Networks (RNNs) makes them particularly suitable for this task?

Recurrent Neural Networks are built to handle sequences of data, and they keep a hidden state that updates as each word in a conversation is read, so they remember what was said earlier. This hidden state acts like a short‑term memory that lets the network use context from previous sentences when predicting the next word. Because a chatbot must understand the flow of a dialogue and generate appropriate replies, an RNN can naturally model the dependencies between earlier and later parts of the conversation. For instance, when a user says “I’m tired,” the RNN remembers that word and can later respond with “Would you like to rest? ” rather than treating the words as isolated tokens.