Setting Up OpenAI Playground

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This article is an excerpt from the book, OpenAI API Cookbook, by Henry Habib. Integrate the ChatGPT API into various domains ranging from simple wrappers to knowledge-based assistants, multi-model, and conversational applications

Introduction

The OpenAI Playground is an interactive web-based interface designed to allow users to experiment with OpenAI’s language models, including ChatGPT. It’s a place where you can learn about the capabilities of these models by entering prompts and seeing the responses generated in real time. This platform acts as a sandbox where developers, researchers, and curious individuals alike can experiment, learn, and even prototype their ideas.

In the Playground, you have the freedom to engage in a wide range of activities. You can test out different versions of the AI models, experimenting with various prompts to see how the model responds, and you can play around with different parameters to influence the responses generated. It provides a real-time glimpse into how these powerful AI models think, react, and create based on your input.

Setting up your OpenAI Playground environment

Getting ready

Before you start, you need to create an OpenAI Platform account.

Navigate to https://platform.openai.com/and sign in to your OpenAI account. If you do not have an account, you can sign up for free with an email address. Alternatively, you can log in to OpenAI with a valid Google, Microsoft, or Apple account. Follow the instructions to complete the creation of your account. You may need to verify your identity with a valid phone number.

How to do it…

1. After you have successfully logged in, navigate to Profile in the top right-hand menu, select Personal, and then select Usage from the left-hand side menu. Alternatively, you can navigate to https://platform.openai.com/account/usage after logging in. This page shows the usage of your API, but more importantly, it shows you how many credits you have available.

2. Normally, OpenAI provides you a $5 credit with a new account, which you should be able to see under the Free Trial Usage section of the page. If you do have credits, proceed to step 4. If, however, you do not have any credits, you will need to upgrade and set up a paid account.

3. You need not set up a paid account if you have received free credits. If you run out of free credits, however, here is how you can set up a paid account: select Billing from the left-hand side menu and then select Overview. Then, select the Set up paid account button. You will be prompted to enter your payment details and set a dollar threshold, which can be set to any level of spend that you are comfortable with. Note that the amount of credits required to collectively execute every single recipe contained in this book is not likely to exceed $5.

4. After you have created an OpenAI Platform account, you should be able to access the Playground by selecting Playground from the top menu bar, or by navigating to https://platform. openai.com/playground.

How it works…

The OpenAI Playground interface is, in my experience, clean, intuitive, and designed to provide users easy access to OpenAI’s powerful language models. The Playground is an excellent place to learn how the models perform under different settings, allowing you to experiment with parameters such as temperature and max tokens, which influence the randomness and length of the outputs respectively. The changes you make are instantly reflected in the model’s responses, offering immediate feedback.

As shown in Figure 1.1, the Playground consists of three sections: the System Message, the Chat Log, and the Parameters. You will learn more about these three features in the Running a completion request in the OpenAI Playground recipe.

Figure 1.1 – The OpenAI Playground

Now, your Playground is set up and ready to be used. You can use it to run completion requests and see how varying your prompts and parameters affect the response from OpenAI.

Running a completion request in the OpenAI Playground

In this recipe, we will actually put the Playground in action and execute a completion request from OpenAI. Here, you will see the power of the OpenAI API and how it can be used to provide completions for virtually any prompt.

Getting ready

Ensure you have an OpenAI Platform account with available usage credits. If you don’t, please follow the Setting up your OpenAI API Playground environment recipe. All the recipes in this chapter will have this same requirement.

How to do it…

Let’s go ahead and start testing the model with the Playground. Let’s create an assistant that writes marketing slogans:

1. Navigate to the OpenAI Playground.

2. In the System Message, type in the following: You are an assistant that creates marketing slogans based on descriptions of companies. Here, we are clearly instructing the model of its role and context.

3. In the Chat Log, populate the USER message with the following: A company that writes engaging mystery novels.

4. Select the Submit button on the bottom of the page.

5. You should now see a completion response from OpenAI. In my case (Figure 1.2), the response is

Unlock the Thrilling Pages of Suspense with Our Captivating Mystery Novels!

Figure 1.2 – The OpenAI Playground with prompt and completion

Note

Since OpenAI’s LLMs are probabilistic, you will likely not see the same outputs as me. In fact, if you run this recipe multiple times, you will likely see different answers, and that is expected because it is built into the randomness of the model.

How it works…

OpenAI’s text generation models utilize a specific neural network architecture termed a transformer. Before delving deeper into this, let’s unpack some of these terms:

• Neural network architecture: At a high level, this refers to a system inspired by the human brain’s interconnected neuron structure. It’s designed to recognize patterns and can be thought of as the foundational building block for many modern AI systems.
• Transformer: This is a type of neural network design that has proven particularly effective for understanding sequences, making it ideal for tasks involving human language. It focuses on the relationships between words and their context within a sentence or larger text segment.

In machine learning, unsupervised learning typically refers to training a model without any labeled data, letting the model figure out patterns on its own. However, OpenAI’s methodology is more nuanced. The models are initially trained on a vast corpus of text data, supervised with various tasks. This helps them predict the next word in a sentence, for instance. Subsequent refinements are made using Reinforcement Learning through Human Feedback (RLHF), where the model is further improved based on feedback from human evaluators.

Through this combination of techniques and an extensive amount of data, the model starts to capture the intricacies of human language, encompassing context, tone, humor, and even sarcasm.

In this case, the completion response is provided based on both the System Message and the Chat Log. The System Message serves a critical role in shaping and guiding the responses you receive from Open AI, as it dictates the model’s persona, role, tone, and context, among other attributes. In our case, the System Message contains the persona we want the model to take: You are an assistant that creates marketing slogans based on descriptions of companies.

The Chat Log contains the history of messages that the model has access to before providing its response, which contains our prompt, A company that writes engaging mystery novels.

Finally, the parameters contain more granular settings that you can change for the model, such as temperature. These significantly change the completion response from OpenAI. We will discuss temperature and other parameters in greater detail in Chapter 3.

There’s more…

It is worth noting that ChatGPT does not read and understand the meaning behind text – instead, the responses are based on statistical probabilities based on patterns it observed during training.

The model does not understand the text in the same way that humans do; instead, the completions are generated based on statistical associations and patterns that have been trained in the model’s neural network from a large body of similar text. Now, you know how to run completion requests with the OpenAI Playground. You can try this feature out for your own prompts and see what completions you get. Try creative prompts such as write me a song about lightbulbs or more professional prompts such as explain Newton's first law.

Conclusion

In conclusion, the OpenAI Playground offers a dynamic environment for exploring the capabilities of language models like ChatGPT. By setting up your account and navigating through its features, you can unlock endless possibilities for creativity, learning, and innovation. Experiment with prompts, adjust parameters, and observe real-time responses to gain insights into AI's potential. Whether you're a developer, researcher, or curious individual, the Playground provides a sandbox for unleashing your imagination and understanding AI's intricacies. With each completion request, you delve deeper into the world of artificial intelligence, discovering its nuances and expanding your horizons. Start your journey today and witness the power of AI in action.

Author Bio

Henry Habib is a Manager at one of the world's top management consulting firms, advising F500 companies on analytics and operations, with a particular focus on building intelligent AI-driven solutions and tools to create impact. He is a passionate online instructor and educator, amassing a of more than 150K paid students and facilitating technical programs at large banks and governmental.
A proponent in the no-code and LLM revolution, he believes that anyone can now create powerful and intelligent applications without any deep technical skills. Henry resides in Toronto, Canada with his wife, and enjoys reading AI research papers and playing tennis in his free time.