Transfer learning is employed to adapt the pretrained models to specific tasks or domains. This involves training the model on a narrower dataset, such as conversations from users interacting with the system. By fine-tuning the model on this dataset, it learns to generate more contextually appropriate responses.

Finally, dialogue models add structure to the conversation by integrating a latent variable that tracks the dialogue history. This helps the model understand the context of the ongoing conversation and generate more coherent responses.

Together, these techniques enable ChatGPT to provide more engaging and responsive conversational experiences.