What is Prompt Engineering

Have you ever wondered how to get more accurate and tailored results from a Large Language Model?

Enter the world of in-context learning. In simple terms, in-context learning allows a model to understand and adapt based on the information you feed it. The more context you provide, the more refined the output.

Unpacking Prompt Engineering

So, where does prompt engineering fit in? Think of prompt engineering as the art of crafting these pieces of context in a way that guides the model toward the desired outcome. It’s essentially in-context learning itself and your direct channel of communication with the model. You can present your problem statements in two broad ways—either with minimal context, known as zero-shot or one-shot prompts, or with additional guiding context, called few-shot prompts.

However, for now, you can park the jargons and realize that each prompting approach has its strengths and limitations, but the aim is the same: to pull the most precise responses out of the LLM.