This resource was adapted with permission from Joseph Deodato and Rutgers University Libraries.
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Text generators are trained on large amounts of text from books, articles, and websites which is analyzed to find patterns and relationships and create new texts by predicting the word or sentence most likely to follow another in a sequence. Text generators can be used to produce a wide variety of content including essays, memos, brochures, poems, songs, and screenplays. |
Source: ChatGPT
Prompt: Explain artificial intelligence in the style of Donald Trump
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Image generators learn by analyzing sets of images with captions or text descriptions. Once they learn which images are associated with which concepts, they can combine them to create new images in a range of styles from photorealistic to abstract. Examples: Dall-E 2 | Midjourney | Stable Diffusion |
Source: Dall-E 2
Prompt: Paint a portrait of Homer Simpson in the style of Edvard Munch's "The Scream"
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Code generators use algorithms trained on existing source code—typically produced by open source projects for public use—and generate new code based on those examples. Some tools can also analyze and debug existing code or offer suggestions for improvement. Examples: CodePal | Tabnine | GitHub Copilot |
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