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While advanced models can handle diverse data types, some excel at specific tasks, like text generation, information summary or image creation. The quality of outputs depends heavily on training data, adjusting the model’s parameters and promptengineering, so responsible data sourcing and bias mitigation are crucial.
Set the right data foundations As a CEO aiming to use generative AI to achieve sustainability goals, remember that data is your differentiator. Figure 5 offers an overview on generative AI modalities and optimization strategies, including promptengineering , Retrieval Augmented Generation , and fine-tuning or continued pre-training.
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