Which prompting technique uses no examples for the model to learn from?

The correct answer is based on the concept of prompting techniques used in generative AI models. Zero-shot prompting is a technique where the model is given a task or question without any examples or prior context to learn from. In this scenario, the model relies entirely on its pre-trained knowledge and understanding of language to generate a response.

This approach is valuable because it tests the model's ability to generalize and apply its training to new and unseen scenarios without direct guidance. Zero-shot prompting is often used when examples may not be available or when it is necessary to assess how effectively the model can draw on its diverse knowledge base to produce coherent and relevant outputs.

In contrast, one-shot and few-shot techniques involve providing either a single example or a few examples to guide the model toward a desired output. Chain-of-Thought (CoT) prompting typically encourages the model to articulate its reasoning process, often using examples to clarify the expected format or logic. However, zero-shot prompting distinctly emphasizes the model's capability to perform tasks independently, enhancing its versatility in generating responses across a broad range of inquiries.