Practical Prompt Engineering 1: Determining the Optimal Number of Few-Shot Samples Through Experimentation

This article introduces prompt engineering as a method to improve the accuracy of LLMs by refining the prompts given to them, rather than modifying the LLMs themselves. It focuses on the Few-Shot learning technique within prompt engineering. The article likely explores how to experimentally determine the optimal number of examples to include in a Few-Shot prompt to achieve the best performance from the LLM. It's a practical guide, suggesting a hands-on approach to optimizing prompts for specific tasks. The title indicates that this is the first in a series, suggesting further exploration of prompt engineering techniques.

• •Prompt engineering improves LLM accuracy.
• •Few-shot learning is a key prompt engineering technique.
• •Optimal sample number in few-shot prompts can be determined experimentally.