![]() ![]() You could be using Curie or Ada or something much lower that does what you need and nothing you don’t. If you want to use these models for a very basic pattern recognition task, you shouldn’t be paying more and using the top model Da Vinci 3. This is because the pricing for each of these models is actually different. You may be thinking, why would I not just use the best one, which is Davinci 3 at the current time? Then we have code-related models here which are more capable of understanding code.Īnd here I have some of my fine tunes from my personal account. It will tell you a little bit of a blurb about what each of these things does and what it’s good at. Open AI has a ton of different models for different purposes.įirst, we have different versions of GPT-3 models like:Īnd all these different ones basically serve different functions. GPT Modelsįirst and probably most importantly, you can change the model that you’re using to interact with. Now, I’ll give you a rundown of the important OpenAI playground settings that you can play around with in the sidebar. OpenAI Playground Settings & How To Use It Therefore, learning how to engineer prompts for the base models through the playground is going to be the focus of this guide. This is because these base-level models are the only things that we can get access to through the APIs currently, and therefore, the only things that we can build businesses on top of. ![]() Long story short, ChatCPT may be fun and valuable in its own right, but if you’re looking to create value and build a scalable business on top of these models, you need to be learning how to engineer the base models in their natural state. The difference is that Open AI has significantly changed GPT-3 in order to make ChatGPT through reinforcement learning and fine-tuning and a bunch of other fun stuff. If you didn’t know, ChatGPT is actually an application that Open AI has built on top of the GPT models that we’re going to be accessing through the playground. This is important to understand because anything that you can achieve within the playground can then be scaled and then productized and sold. If you’re unfamiliar with the playground, it provides us with a flexible platform where we can interact with all of the Open AI suite of products in their natural state.Īnd by natural state, I mean the form that we get access to them through the Open AI APIs. It is crucial to understand that the playground is not the same as Chat GPT. Chat GPT vs OpenAI Playgroundįor this prompt engineering guide, we will be using the Open AI Playground for our prompting. Your ability to write great prompts directly determines your ability to extract value from them. When you have large language models like GPT-3 that are massive and are just a soup of data. The reason prompt engineering, or more simply put, how you construct your prompts, is so important and so valuable is because of a concept called garbage in, garbage out.Įssentially, the quality of your input determines the quality of your output. ![]() However, most of the value created through prompting is not done with Chat GPT. The text that you provide in that dialog box is your prompt. I’m sure you’ve all played around with Chat GPT. ![]() Prompts can vary in complexity from a phrase to a question to multiple paragraphs worth of text. We tell the AI, for example, GPT-3, a set of instructions, and it performs the task based on those instructions. In plain English, prompting is the process of instructing an AI to do a task. ![]()
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