From chatbots to neural networks, understanding these AI terms becomes more important every day.
By 2025, artificial intelligence is nearly ubiquitous. From business software providers adding AI capabilities to their platforms to people using ChatGPT for therapy, the technology has become increasingly ingrained in daily life.
As a result, the new jargon and buzzwords that have circulated in the tech industry over the past few years are beginning to enter more casual environments, and understanding them is becoming increasingly important for those who want to stay informed.
In this glossary guide, we will explain some of the AI terms you may or may not have heard over the past few years, hoping to provide some context when the topic inevitably arises in your business.
AI (Artificial Intelligence)
AI stands for artificial intelligence and is a broad term for any technology that seeks to replicate human intelligence. More specifically, AI refers to technology capable of thinking and learning like humans.
Algorithm
An algorithm is a set of instructions provided to an AI model or any software that determines how it operates. For example, the TikTok algorithm describes how the social media platform functions for a specific user.
Chatbot
A chatbot is AI-powered software designed to replicate human interaction through messaging or voice-enabled platforms.
Data Mining
Data mining is the practice of collecting large amounts of data from users, with the suggestion that this data be used to identify patterns and inform software on how to better serve users.
Deepfake
A deepfake is an image or video featuring the likeness of a real person, generated entirely by AI. This technology is highly dangerous and, in 2025, is making scams increasingly difficult to detect.
Generative AI
Generative AI describes any AI program that generates something, including written copy, images, and videos. Most of the AI in the news today is generative AI, including tools such as ChatGPT and Gemini.
Hallucination
An AI hallucination is defined as a common occurrence in which an AI platform provides incorrect, false, or misleading answers to user queries. Unfortunately, AI errors have become quite common for the software, but it is hoped that they will improve over time.
Large Language Model (LLM)
A large language model (LLM) describes an AI model specifically trained on massive text datasets so that it can understand natural language prompts and, in turn, produce similarly human-like responses.
Machine Learning
Machine learning is a type of AI that focuses on specific models that learn in a manner similar to humans. As a result, programs with machine learning will improve and grow over time without human intervention.
Natural Language Processing (NLP)
Natural language processing (NLP) is defined as the type of AI processing that focuses on understanding queries and providing answers that reflect the type of language used by real humans in conversation.
Neural Network
A neural network is a form of AI processing that resembles the human brain, using connected nodes or neurons organized in layers. This allows the AI to engage in deeper "thinking," though it does require large datasets to accomplish this.
Prompt
In the context of AI, a prompt is the text you input to an AI model. Prompts can take various forms, including questions, queries, or commands, and can incorporate many different elements such as links, images, or videos.
Recognition
When discussing AI, recognition generally refers to image recognition, where a user inputs an image or video and AI technology is used to identify certain elements within it.
Token
A token is an allocation used for prompts on AI platforms. Essentially, AI platforms like ChatGPT only allow users a certain number of tokens, depending on how much they pay. Tokens are also referred to as "credits" or even simply "messages," depending on the platform.
Training Data
Training data describes the vast datasets that AI companies use to train their models, with the hope of making them increasingly intelligent over time.
Vibe Coding
Vibe coding is a term used to describe coding without actually using a coding language. It allows users to create applications, websites, and software through simple plain-text prompts. This means that almost anyone can now code, thanks to AI.