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Terms related to generative AI that you might find useful.

Harold Robert Meyer and The ADD Resource Center 04/23/2025

“Artificial intelligence is not merely a technological advancement; it is the ultimate force multiplier for human potential, broadening what is achievable for individuals, enhancing national competitiveness, and equipping humanity with powerful tools to tackle our greatest global challenges.” Harold Meyer

Term	Definition
Prompt	The text or other input you provide to a generative AI model to tell it what you want it to create (e.g., “a cat wearing a hat,” “write a short story about a robot”).
Hallucination	When a generative AI model produces information that is false, nonsensical, or not based on the training data or the provided prompt. It might present this incorrect information confidently.
AI-generated content	Any text, images, audio, video, or other media that has been created by artificial intelligence models.
Style transfer	A technique where the artistic style of one image is applied to the content of another image (e.g., making a photo look like a Van Gogh painting).
Deepfake	A manipulated video or audio recording that realistically depicts someone saying or doing something they did not actually say or do. Often created using generative AI.
Latent space	Think of this as a hidden, abstract space where the AI stores compressed representations of the data it has learned. By navigating this space, the AI can generate new variations.
Training data	The large amounts of real-world data (text, images, etc.) that are used to teach a generative AI model how to understand patterns and create new content.
Bias	When the training data used to create an AI model contains prejudices or imbalances, the model can learn and perpetuate these biases in its generated output.
Fine-tuning	The process of taking a pre-trained AI model and further training it on a smaller, more specific dataset to make it better at a particular task or style.
Model	In the context of AI, a model is the learned representation of the data that the AI uses to make predictions or generate new content.
Parameters	The adjustable variables within an AI model that are learned from the training data. Models with more parameters are often more complex and capable.