RNNs (Recurrent Neural Networks) work by sequentially processing input data, one step at a time. At each step, the RNN takes an input and produces an output. It also maintains a hidden state, which acts as a memory of the previous steps. The hidden state is updated and passed along to the next step, allowing the RNN to consider the context and information from previous steps while making predictions or generating outputs. This sequential nature of RNNs enables them to capture patterns, dependencies, and context in sequential data, making them useful for tasks like language modeling, speech recognition, and time series analysis.