Prompt Engineering to Leverage In-Context Learning in Large Language Models

<p>Large Language Models&nbsp;are more and more used and their skills are surprising. Part of their success is their ability to learn from a few examples, a phenomenon known as&nbsp;in-context learning; in the previous article, we discussed in detail what is it and from where it originates, now we will learn how to harness the true power.</p> <h2>All You Need to Know about In-Context Learning</h2> <h3>What is and how does it work what makes Large Language Models so powerful</h3> <p>towardsdatascience.com</p> <p>This article is divided into different sections, for each section we will answer these questions:</p> <ul> <li>A brief recap on in-context learning</li> <li>How do you interact with a model? Which element should be inserted? Can changing the prompt impact the answer?</li> <li>How can we increase the ability of a model in ICL? What is Zero-shot or few-shot prompting? What is Chain-of-thought (COT) or zero-shot COT? How do you get the best from your COT? Why LLMs Can Perform CoT Reasoning?</li> <li>What is the tree-of-thoughts?</li> <li>Can we automatize this process?</li> </ul> <p>Check the list of references at the end of the article, I provide also some suggestions to deepen the topics.</p> <p><a href="https://pub.towardsai.net/prompt-engineering-to-leverage-in-context-learning-in-large-language-models-72296e1f09c3">Website</a></p>