Header image generated on Midjourney with prompt itachi coding
The Open AI Playground is a web-based tool. The GPT-3 playground makes things much easier for calling an API without writing a single line of code. We can give the requirement as a text input in plain English. The Playground allows users to explore and customize various model settings and parameters, giving them greater control over the behavior of the AI models. This can be useful for researchers and developers who want to fine-tune the models for specific applications or use cases.
Sign In GPT-3 Playground
- Create your account on the OpenAI website. Click on get started and create your account by providing details. (If you already have an account created in Open AI for ChatGPT, the same account can be used)
Exploring components of GPT-3 Playground Console:
Open AI GPT-3 Playground consists of a few components, which we will discuss in detail below.
Prompts and completions
The prompts and completions are an easy interface where you can feed your input. These models are highly flexible and powerful. If we give any text as input in the prompt, a text completion will be generated by the model that matches the context or pattern of the input you have provided.
Tokens
The models understand the process by breaking down the components in a text as tokens. Big words will be divided again into multiple parts for Ex: "e-commerce" will be divided into “e”, “-” and “commerce”, while smaller words like "essay", "and" etc are taken as a whole.
Click here to try it yourself.
In general, the number of tokens >= number of words. Billing is done on the basis of tokens. So it is best practice to always keep the number of tokens as low as possible.
Models
The GPT-3 playbook is powered by four models: Davinci, Curie, Babbage, and Ada. Davinci is the most capable model with the greatest performance but comes with a higher cost, and on the other hand, Ada is the fastest model with a lower cost. Please check out the documentation for comprehensive details.
Temperature
The results will be less random if the temperature is close to zero, and if the temperature is close to 1, the output will be more unpredictable and creative.
here is an example :
When the Temperature is 0.5
When the temperature is 1
Maximum length
Here we specify the length of the output. For example, if we want one-word answer we will keep the maximum length at 2.
It is good for summarization purpose.
Top P
When generating text using GPT-3, the model considers all possible next words and assigns a probability to each one based on its training data. The "top p" parameter determines the threshold probability above which the model will consider a word as a candidate for the next word in the generated text sequence.
For example: if the "top p" value is set to 0.9, the model will only consider words with a probability higher than 0.9 as candidates for the next word.
Change this setting to see the probability.
Best of
The "best of" setting allows the user to specify how many of these different versions should be generated, and which one should be selected as the "best" or most appropriate version to display.
For example, if the "best of" setting is set to 3, the model will generate three different select the one with the highest likelihood as the "best" version to display.
To create no different version, the model will run multiple times, thereby increasing the cost multiple times.
Export settings with model detail
click at view code
What Are the Key Features of GPT-3 Playground?
If you are still confused about where to start or have trouble coming up with an idea, there will be a preset menu in the top right corner.
There are more advanced options in the right sidebar, from which we can choose the mode in which the tool will help in editing the text by adding or removing something that’s already been written. It will also give you more control over the output.
Conclusion
The article has covered the playground and its major settings and components.
I hope it will be helpful.