Unlocking the Full Potential of Large Language Models like ChatGPT with Expertly Crafted Prompts.

Table of contents

• Introduction.
  • What’s with the whole Buzz?
• The Importance of Prompt Engineering.
• Strategies for Effective Prompt Engineering.
  • How Prompts Influence Context, Tone, and Style of Generated Text.
  • Elements and Basics of a Prompt.
  • Prompting Techniques.
• Best Practices for Creating the best prompts.
• Conclusion.
• References.

Introduction

Hey there! If you've been keeping up with the latest news, you must have seen a lot of buzz on social media about new models being released. One of them is ChatGPT, which caught the attention of the whole world since November of 2022, especially young folks. It became this sort of play toy in every youngster's itinerary, the test to see what it could generate and what responses it could give.

As someone who is into ML and particularly Natural Language Processing, I personally believe that we haven't gotten to the point where AI can function to take over every job. At the crux of it, a human still has to be the one to input commands that it would output. But let's face it, it's still a lot of fun to play around with.

What’s with the whole Buzz?

https://www.polygon.com/23177499/lightyear-sandwiches-director-pixar

Well, we’ve come to an era where language models like ChatGPT, Bard, Llama, Flan, and BloombergGPT have been equipped with superhuman-like abilities to be able to generate information irrespective of the input we give it that can help us solve tasks, summarize essays, and in some cases write code (though I won’t always rely on this).

To clear things up, Large Language Models are models consisting of a neural network with many parameters (typically billions of weights or more), trained on large quantities of unlabeled text using self-supervised learning. In a nutshell, they are computer algorithms with probabilistic capabilities that are trained on tons and tons of data available on the internet or personally accumulated data. In reference to other forms of machine learning, there is no target variable, the algorithm learns to find its own patterns and relationships in these vast amounts of data.

The result of this is a very smart kid that seems to know everything but at the same time still exhibits symptoms of being a kid in that it tends to make mistakes and you can’t always rely on its output.

As we do for kids, they often need the hands of experts or people to guide them on how to make good use of their talents which is the whole idea of Prompt Engineering.

According to promptingguide.ai,

Prompt engineering is a new way to make prompts that work better with language models (LMs). It is the approach of designing optimal promps to instruct the model to perform a task. This can help with many different tasks, like answering questions or doing math problems. People use prompt engineering to make LMs work better and to create new tools that work with them.