Which of the following statements about prompt-based development is correct?

Prompt-based development is indeed generally much faster than supervised learning. This approach leverages existing large language models that have been pre-trained on extensive datasets. By simply crafting a well-designed prompt, practitioners can utilize these models for a variety of tasks without the need for extensive retraining. This efficiency stems from the fact that there is no need for the lengthy and resource-intensive process of collecting and labeling large datasets, which is typical in traditional supervised learning scenarios.

The other statements do not align with the principles of prompt-based development. For instance, while supervised learning relies heavily on numerous labeled examples to train a model, prompt-based development can often achieve satisfactory results with fewer or even no specific examples, depending on the complexity of the task and the prompt design. Similarly, collecting large amounts of unlabeled data is not a primary requirement in this context. Lastly, the assertion that it is impossible to craft a prompt for classifying reviews into categories like positive, neutral, or negative overlooks the flexibility and adaptability of prompt-based methods, which can indeed be structured to perform such classifications effectively.