In this writing, I explore using ChatGPT, a text-based generative AI model, as a color suggestion tool for building qualitative color schemes for your data visualizations. In earlier writings, I have discussed how to use ChatGPT to build: (a) sequential color schemes that are designed for ordering numeric information where colors progress from low to high (or vice versa) and (b) divergent color schemes that fuse two sequential color schemes, each with different hues, together with a shared neutral midpoint color.

A qualitative data color scheme uses distinct colors to label different categories of data. There are no implied magnitude differences between each of the categories and no large variances in lightness or saturation. Successful results are based on: (1) the specific text phrasing used to request ChatGPT to perform these tasks and (2) verifying the chatbot recommendations since not all suggestions are correct.

What is ChatGPT?

Chat Generative Pre-trained Transformer or ChatGPT is a Generative Artificially Intelligent (AI) chatbot developed by OpenAI and introduced to the public on November 30, 2022. Using text-based commands, users can ask the chatbot for assistance in tasks such as creating emails, essays, and code. There are numerous reviews that describe the functionality of ChatGPT and other text-based AI tools. The basic version of ChatGPT 3.5 is free to use with OpenAI offering a “Plus” version of ChatGPT 4.0 for $20 a month. For this writing, I am using the free ChatGPT 3.5 version. I show the user interface and information about ChatGPT pricing below.

Asking ChatGPT to Assist in Creating Qualitative Color Scheme Suggestions:

Like my previous sequential and diverging data scheme explorations with ChatGPT, my efforts here are to determine if the chatbot can define the concept of qualitative data color schemes and provide potential suggestions in color Hex code format. Since ChatGPT is a text-based generative AI model…