When generating data for prompt tuning in IBM watsonx, which of the following is the most effective method for ensuring that the model can generalize well to a variety of tasks?
Option A here. The trap is D, but one detailed prompt can’t replace real diversity across tasks and complexity. Diversity in training data is always key for solid generalization, at least from what I’ve seen in practice. Agree?
Pretty sure it's A. Practice tests and the Watsonx docs both push for diverse prompts spanning multiple domains to get better generalization, not just covering a single use case or repeating patterns. Always saw this idea pop up in the official prep material too. Someone correct me if that's off.
Does anyone actually see a benefit in picking D? I get the idea of one detailed prompt, but in practice, variety trumps depth when it comes to generalizing across tasks. Using just one scenario seems way too limited for prompt tuning.