Remove 2040 Remove AI Modeling Remove Prompt Engineering
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Generative AI use cases for the enterprise

IBM Journey to AI blog

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 prompt engineering, so responsible data sourcing and bias mitigation are crucial.

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The executive’s guide to generative AI for sustainability

AWS Machine Learning Blog

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 prompt engineering , Retrieval Augmented Generation , and fine-tuning or continued pre-training.

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