D. Use a higher temperature during the generation process You have completed a prompt -tuning experiment for a large language model (LLM) using IBM Watsonx, aimed at improving its ability to generate accurate responses to customer support queries. After the tuning process, you are analyzing the performance statistics of the model. Which statistical metric is the most appropriate to prioritize when evaluating the success of the prompt -tuning experiment?
You are working with a Watsonx Generative AI model to create marketing content that balances creativity with efficiency. The goal is to generate engaging content within a predefined time limit without compromising on quality. Given this context, which two optimization strategies will most effectively help you achieve both speed and content quality? (Select two)
prompt is tracked and accessible for rollback in case a newer version produces worse results. Which strategy would best ensure that all prompt versions are stored and easily retrievable, while minimizing disruption to the current deployment?
how well the tuned model generalizes to unseen data?
B. Data Privacy Officer C. AI Model Developer D. Chief Technology Officer (CTO) As a Generative AI engineer, you're tasked with optimizing the performance and cost -efficiency of a model by adjusting the model parameters. Given that your objective is to reduce the cost of generation while maintaining acceptable quality, which of the following parameter changes is most likely to result in cost savings?
➢ TOTAL QUESTIONS: 379 In the context of IBM Watsonx and generative AI models, you are tasked with designing a model that needs to classify customer support tickets into different categories. You decide to experiment with both zero-shot and few -shot prompting techniques. Which of the following best explains the key difference between zero -shot and few -shot prompting?
