From Few-Shot to Guidelines: A Smarter Way to Prompt AI

In the world of large language models (LLMs), one of the biggest challenges is how to get the best possible response from the AI.

Traditionally, "shot" methods have been the go-to, where a model is given example questions and answers, like in few-shot learning. This approach prompts the AI to mimic the reasoning steps in the examples. However, this method isn’t without its downsides—it’s hard to choose the right examples, and sometimes, even the best examples can miss crucial task-specific knowledge.