A software team is comparing two natural language processing approaches for a customer support chatbot. Approach one uses a large pretrained language model that can generate open-ended, fluent responses to a wide variety of unscripted questions. Approach two uses a smaller model trained only to match customer questions against a fixed set of predefined intents and reply with scripted answers. Which statement correctly distinguishes generative AI from this second, non-generative approach?

The statement that generative artificial intelligence produces new, original text output rather than selecting from a predetermined set of fixed responses is correct. Generative models create new content, such as original sentences composed word by word based on learned patterns, which is why the large pretrained language model approach can respond fluently to unscripted questions. The smaller intent-matching model, trained to match questions to predefined intents, simply selects the closest match from a fixed library of prewritten answers and cannot produce anything outside that set. The claim that generative artificial intelligence can only process numeric data is incorrect because generative language models process and produce text directly, not just numeric data; numeric representations are an internal implementation detail, not a limitation on the model's input or output type. The claim that generative artificial intelligence requires no training data at all is incorrect because generative models absolutely require large amounts of training data to learn language patterns before they can generate anything; the idea that they need no training data is false. The claim that generative artificial intelligence is only used for image tasks is incorrect because generative artificial intelligence is not limited to images, as demonstrated in this very scenario by a generative text model handling customer support questions, and intent-matching systems are not defined by working only with text either.