Which type of prompting technique offers multiple examples to guide the model?

The type of prompting technique that offers multiple examples to guide the model is few-shot prompting. This approach involves providing the model with several examples of the desired task within the prompt itself. By doing so, the model is better equipped to understand the context and nuances of the task at hand, as it can learn from these specific instances and apply similar reasoning to new instances during generation.

Few-shot prompting can be particularly effective for tasks where the model may not have been explicitly trained or where it needs further context to refine its outputs. The inclusion of multiple examples helps clarify the task, improving the quality of the model's responses by showcasing different variations of input-output pairs.

In contrast, zero-shot prompting does not provide any examples at all, and one-shot prompting presents only a single example. Chain-of-thought prompting, while useful for complex reasoning and processes, does not inherently focus on providing multiple examples but rather encourages the model to articulate its reasoning step by step. This distinction makes few-shot prompting particularly valuable when users want to leverage examples to enhance the model’s understanding.