What technique involves providing explicit examples in a prompt to guide an LLM's response?

What technique involves providing explicit examples in a prompt to guide an LLM's response?

• A. Zero-shot prompting
• B. One-shot prompting
• C. Few-shot prompting
• D. Multi-shot prompting

Answer: (C)

The technique that involves providing explicit examples in a prompt to guide a Large Language Model's (LLM) response is known as few-shot prompting. This method allows the model to see a small number of examples of the task it is expected to perform, thereby helping it to understand the desired output format and context. By including these examples, users can significantly enhance the performance of the model on the given task, as it gives the model context and a clearer idea of what is expected in its response. For instance, if the task is to translate sentences, providing a few examples of sentences and their translations helps the model mimic that style and follow any specific patterns demonstrated by the examples. This is particularly useful when the user has limited data or does not want to go through the effort of providing an extensive dataset for fine-tuning. In contrast, zero-shot prompting does not provide any explicit examples, relying solely on the model's pre-existing knowledge. One-shot prompting provides only one example, which may not be sufficient to establish a clear pattern. Multi-shot prompting, while not a standard term, might imply more examples than few-shot prompting, potentially offering an even broader context but straying from the specific definition of few-shot prompting. Therefore, few-shot prompting is the

The technique that involves providing explicit examples in a prompt to guide a Large Language Model's (LLM) response is known as few-shot prompting. This method allows the model to see a small number of examples of the task it is expected to perform, thereby helping it to understand the desired output format and context. By including these examples, users can significantly enhance the performance of the model on the given task, as it gives the model context and a clearer idea of what is expected in its response.

For instance, if the task is to translate sentences, providing a few examples of sentences and their translations helps the model mimic that style and follow any specific patterns demonstrated by the examples. This is particularly useful when the user has limited data or does not want to go through the effort of providing an extensive dataset for fine-tuning.

In contrast, zero-shot prompting does not provide any explicit examples, relying solely on the model's pre-existing knowledge. One-shot prompting provides only one example, which may not be sufficient to establish a clear pattern. Multi-shot prompting, while not a standard term, might imply more examples than few-shot prompting, potentially offering an even broader context but straying from the specific definition of few-shot prompting. Therefore, few-shot prompting is the