Developing and applying generative AI models is not an easy task. It involves many challenges, such as data quality and availability, ethical and social implications
Generative AI systems may not always produce high-quality outputs, and the generated content may not be suitable for the intended use case.
Generative AI systems are typically trained on a dataset and can generate new content based on that dataset. However, it can be challenging to control the output of these systems.
Generative AI models can be computationally expensive to train and run, requiring significant computational resources.
Generative AI models require large amounts of data to train effectively, which can raise concerns about data privacy and security.
Generative AI models can perpetuate biases present in the training data, leading to unfair or discriminatory outcomes.