As far as I am aware, OpenAI has not publicly disclosed the architecture of ChatGPT, their open-source chatbot model. In general, chatbots like ChatGPT use a combination of natural language processing (NLP) and machine learning techniques to generate responses to user inputs.

One common technique used by chatbots to "remember" previous prompts is to use a sliding window approach, where the model maintains a fixed-size buffer of the most recent inputs and uses this information to generate its responses. This allows the model to take into account the context of previous inputs, but does not provide true recurrence or long-term memory capabilities.

Another approach that some chatbots use is to incorporate recurrence into the model architecture, such as through the use of recurrent neural networks (RNNs) or long short-term memory (LSTM) networks. These architectures are designed to allow the model to maintain a "memory" of previous inputs over longer periods of time, which can help improve the coherence and consistency of the generated responses.

It is not clear which approach, if any, OpenAI has used in the development of ChatGPT. The company has not publicly released details about the model architecture or the techniques used to implement memory in the model.