A development team is tasked with adapting a pre-trained language model for a new application. The model was originally trained on text segments with a maximum length of 1,024 tokens. The new application requires the model to process and maintain context over dialogues that are often 3,000 tokens long. To prepare the model, the team fine-tunes it using a large, high-quality dataset where each example is a short, self-contained text segment, averaging 500 tokens. Upon deployment, the team observes that the model performs poorly on the longer dialogues, frequently losing track of earlier parts of the conversation. Based on this information, analyze the team's methodology and identify the primary flaw in their fine-tuning strategy. What specific characteristic should their fine-tuning data have possessed to better achieve their goal?