Which action can significantly improve the effectiveness of a Generative AI model in summarizing customer feedback?

Regularly retraining the model to adapt to new feedback topics is crucial for enhancing the performance of a Generative AI model, particularly in summarizing customer feedback. Customer preferences, trends, and language can evolve rapidly, meaning that a model trained solely on past data may not accurately reflect current sentiments or commonly used phrases. By incorporating new data through retraining, the model can learn to recognize emerging topics and adjust its summarization techniques accordingly, leading to more relevant and accurate outputs.

This continuous learning process helps in maintaining the model's relevance and effectiveness, ensuring that it aligns well with the latest customer insights and preferences. In contrast, options such as limiting input variables could restrict the model's ability to capture the nuances of customer feedback, while relying on outdated models can cause it to miss important context and trends. Lastly, focusing only on historical data may lead to a static understanding that fails to keep up with the dynamism of customer sentiments.