Response by generation is an encoder-decoder task that learns from a corpus to transduce a user's prior question into an acceptable answer. Basic encoder-decoder models tend to yield very simple responses like "Okay" or "I'm fine," but one way to work around this is to fine-tune a large language model on a conversational dataset and using the model to directly generate responses.