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Answer :
The statement that is not true about generative AI (GenAI) is: 'GenAI always provides factually accurate answers.'
Explanation:
GenAI's Output and Training Data: Generative AI models are trained on vast amounts of data, and they learn to generate outputs based on the patterns and structure of this data. The training process involves feeding the AI model with examples, which it uses to learn how to produce similar outputs. This means the quality and nature of the output are influenced by the data it was trained on.
Factually Accurate Answers: Generative AI does not guarantee factually accurate answers. While these systems can produce outputs that are grammatically correct and often contextually relevant, they can also produce erroneous or misleading information. This is because they do not have an understanding or awareness of factual accuracy, but rather generate outputs based on learned patterns.
User Input and Prompts: User inputs, commonly known as prompts, play a crucial role in guiding the outputs produced by generative AI. These inputs are the starting point for the model to determine the type of response it generates. The quality and specificity of the prompts can significantly affect the relevance and accuracy of the responses.
Follow-up Prompts: The output of generative AI can be refined and modified through subsequent prompts. Users can iterate and interact with the model to hone in on more desired outcomes. This process allows for the adjustment and improvement of outputs, making it a powerful feature of GenAI systems.
Overall, while generative AI can be a valuable tool for generating language and content, it is crucial for users to critically evaluate and verify the information it produces.
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