Beginner's Guide to Generative AI: Home

Generative AI, often called GenAI, is a type of artificial intelligence that can create output based on training data. Output may include text, audio, image, or video output. ChatGPT is probably the most well-known of these Generative AI tools, but there are many others rapidly being developed.

The tools can create increasingly sophisticated output and are expected to rapidly impact fields such as business, journalism, art, and education. Experts are both excited about the possibilities this new technology creates and concerned about some of the ethical questions surrounding its use.

For a quick intro to how Generative AI technology works, we recommend the short video "What is Generative AI?" from Linked in Learning (UNC Onyen log-in required.)

For a deeper dive into the subject, the explanatory video "Generative AI in a Nutshell" by Henrik Kniberg is a good option.

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Defining Generative AI
To understand generative artificial intelligence (GenAI), we first need to understand how the technology builds from each of the AI subcategories listed below.
Artificial Intelligence, the broadest category, is the theory and methods to build machines that think and act like humans (shows an illustration of a brain with brightly colored areas). Programmers teach AI exactly how to solve specific problems by providing precise instructions and steps.
Machine Learning, a subcategory of Artificial Intelligence, is the ability for computers to learn from experience or data without human programming (shows an illustration of a laptop with a lightbulb on the screen).
Deep Learning, a subcategory of Machine Learning, mimics the human brain using artificial neural networks such as transformers to allow computers to perform complex tasks (shows an illustration of a network with nodes and connections).
Generative AI, a subcategory of Generative AI, generates new text, audio, images, video or code based on content it has been pre-trained on (shows decorative icons beside ChatGPT, Midjourney, and Bard).

This image is from (AI for Education).

These definitions were compiled vebatim from multiple sources (see citations for more information).
 

Agents: Software entities that can plan, act, and adapt on your behalf. ¹

Algorithm: A step‑by‑step set of rules a computer follows to solve a problem ¹

Artificial Intelligence: The use of technology to simulate human intelligence, either in computer programs or robotics. A field in computer science that aims to build systems that can perform human tasks. ²

Bias: In regards to large language models, errors resulting from the training data. This can result in falsely attributing certain characteristics to certain races or groups based on stereotypes. ²

Chatbot: A program that communicates with humans through text that simulates human language. ²

Deep Learning: subset of Machine Learning that uses multi‑layered neural networks to automatically learn patterns from large amounts of data—key to today’s GenAI. ¹

Ethical Considerations: An awareness of the ethical implications of AI and issues related to privacy, data usage, fairness, misuse and other safety issues. ²

Generative AI: AI systems that create new content—text, images, code, music—rather than just analyze existing data. ¹

Hallucination: An incorrect response from AI. Can include generative AI producing answers that are incorrect but stated with confidence as if correct. ²

Large Learning Model (LLM): An AI model trained on mass amounts of text data to understand language and generate novel content in human-like language. ²

Machine Learning: Algorithms that improve at a task through data and experience, rather than explicit rules coded by humans.¹

Multimodal AI: Models that can process and generate multiple data types—text, images, audio, video—within the same architecture. ¹

Neural Network: A web of interconnected nodes (“neurons”) inspired by the brain. Layers of neurons learn to detect patterns, from edges to faces to words. ¹

Natural Language Processing (NLP): A branch of AI that uses machine learning and deep learning to give computers the ability to understand human language, often using learning algorithms, statistical models and linguistic rules. ²

Prompt: The input you feed an AI model. It can be a question, an instruction, or a chunk of data to transform. ¹

Prompt Engineering: Crafting prompts (structure, wording, examples) to reliably get the model output you want. ¹

Retrieval-Augmented Generation (RAG): A workflow where a model first retrieves relevant documents from a database, then uses them to generate an informed answer—reducing hallucinations. ¹

Turing Test: Named after famed mathematician and computer scientist Alan Turing, it tests a machine's ability to behave like a human. The machine passes if a human can't distinguish the machine's response from another human. ²

¹ GENAI Glossary: 40 Key terms You Need to know in 2025. (n.d.). GenAI Glossary: 40 Key Terms You Need to Know in 2025. https://usefulai.com/glossary

² Khan, I. (2025, July 7). ChatGPT Glossary: 53 AI Terms everyone should know. CNET. https://www.cnet.com/tech/services-and-software/chatgpt-glossary-53-ai-terms-everyone-should-know/

Other AI Glossary sites

Aiprm. (n.d.). AIPRM’s Ultimate Generative AI Glossary. AIPRM. https://www.aiprm.com/ai-glossary/

Melnyk O, Ismail A, Ghorashi NS, Heekin M, Javan R. Generative Artificial Intelligence Terminology: A Primer for Clinicians and Medical Researchers. Cureus. 2023 Dec 4;15(12):e49890. doi: 10.7759/cureus.49890. PMID: 38174178; PMCID: PMC10762565.