The ULTIMATE Beginner's Guide to Prompt Engineering with GPT-4 | AI Core Skills
prompt engineering is by far one of the highest leverage skills that you can learn. In 2023 those equipped with it are capable of creating millions of dollars worth of. value in just a few carefully crafted sentences. This guide is intended for anyone. looking to add this foundational skill of prompt engineering to their toolkit so they can access more opportunities with an AI. We will be using the open AIS playground for our prompting. The playground is not the same as chat GPT. It provides us with a flexible platform that we can interact with all of the open AI Suite of products in their natural state. This is because these base level models are the only new things we can build businesses on top of therefore learning how to engineer prompts for the base models through the playground is going to be the focus of this video. We start with a very simple prompt that uses the power of large language models like gpt3 and its understanding of language to convert a. mixed bag of first and last names and order them in order. Next up we go through a carefully crafted. prompt that allows us to remove personal information from our email. The Prompt says read the name and replace it with the appropriate placeholder for replace the name. Remove the phone number and address as well as being able to remove any personally identifiable information and then we have pasted in a sales email if we submit the response we get back the same response with all the personal information removed. We then take the same exact prompt and apply a larger scale within the playground to achieve the same result within the larger scale of a larger sales email as well. We end up with a larger and more successful sales email that we were able to achieve with the prompt that we've pasted with the same amount of zeros even if there are many and just like that we have. the correct answer of 9 billion as you can see changing your prompting just a. little bit can have drastic effects on having a correct or incorrect answer while extremely. obvious on simple math tasks like this these slight prompt differences can have an enormous. effect on more complex tasks. It's also important to understand that the playground can then be scaled and then productized and sold more on that. later if you didn't know chat TPT is actually an application that openai has built. ontop of the gpt1 model that we're going tobe accessing through the Playground. It allows you to remove the personal info from any sales email and then it's also known to remove and replace the number and number of phone numbers and then remove the names and addresses it's known to. The difference is thatopenai significantly changed Gpt3 in order to make chat G PT the. reinforcement learning and fine-tuning and a bunch of other fun stuff long story short chat. GPT may be fun and valuable in its own right but if you're looking to. create value and build scalable business onTop of these models you need to be. learningHow to engineer the base model in theirnatural state this is the core focus of prompt. engineering within this video to learn how to use the playground for your prompting. You don't need any coding experience to learn prompt engineering. You can also take a step by step through learning it but also share with you exactly how myself and others have been using the skill to make money and build. businesses and no you don’t need no coding experience this guide is meant for anyone looking to adding this foundational skills to theirtoolkit. We are going to take you through a simple example here in open Ais playground here on. screen we have a basic Mouse equation and if I submit that you'll see that. it actually comes back with an incorrect answer this simple error can easily be fixed. with a little bit of Prompt engineering here I've added make sure to put the. right amount ofZeros evenIf there aremany and just if there is many even if you are many. We're then going to Output this in last first first Mike then it says John Doe otley Peterson Liam Liam Liam then it’s then it reads John Doe John Doe and then read John Doe. We’re going to go to Output next up we’ve given John Doe Otley Peterson John Doe Liam Liam and then the same email with all of the personal information is removed and we have read the first first names and the last first name. We can then go to the next step in the process and read the second first first name and the name of John Doe so it can be pasted into the sales email. We get back to Output we can read the same prompt that is being used to read the sales response and read a sales response. We have the same example here we have on screen a verysimple prompt that can be used to create a larger. response.