embedding models?
You are tasked with deploying a suite of AI assets, including a pretrained generative language model and a set of custom -trained models. Your company operates in an environment where scalability and adaptability are key, as customer needs vary significantly across regions and sectors. You need to ensure that the deployment of these AI assets can meet varying demand, maintain performance, and allow for customization based on specific client needs. Which deployment strategy best balances scalability, performance, and adaptability for this suite of AI assets?
You are tasked with fine-tuning a generative AI model for text data using synthetic data created through the IBM watsonx platform's user interface. The data you are working with is skewed, containing mostly outliers, and you need to ensure that the synthetic data mimics the distribution accurately. Which algorithm would be most appropriate for generating synthetic data that mirrors the original distribution, considering the Anderson -Darling test for normality?
You're designing a prompt that should generate text for product descriptions using a generative AI model. You want to limit the model’s word choices to ensure coherence while still allowing some flexibility for creativity. You decide to use Top -P (nucleus) sampling to achieve this. Which of the following settings for the Top-P parameter is most appropriate to strike a balance between creativity and coherence?
You have been assigned the task of fine -tuning a large language model (LLM) for a chatbot that will assist users with technical troubleshooting. The goal is to ensure the chatbot responds accurately to user queries, but also in a specific tone and format. Which of the following steps is the first critical phase in the InstructLab workflow to ensure successful customization of the model?
In the context of prompt engineering for IBM Watsonx Generative AI, which of the following is the most accurate description of a prompt variable?
