A glossary of key terms that are used in today’s generative artificial intelligence (GenAI) profession.
GenAI Terms – A to Z Order
Agentic AI
An AI program designed to perform tasks, make decisions, and interact with other tools or services to achieve a specific goal. Agents can be autonomous or semi-autonomous and are often used to automate complex workflows.
MCP or Model Context Protocol is a standard that provides a common language for AI agents (clients) to converse with external sources (servers) such as data, tools and services. It allows AI models to go beyond their training data to access real-time information.
Self-driving cars are a good example of an autonomous AI agent – they rely upon sensory inputs, GPS, and driving algorithms to safely navigate a roadway.
Alignment
The process of ensuring that a generative AI model’s outputs and behavior are consistent with human values, ethics, and intended goals. This is a critical area of research to prevent models from generating harmful or undesirable content.
Artificial Intelligence (AI)
In the digital marketing profession, AI involves the use of machine learning and natural language processing to improve the effectiveness and efficiency of various marketing strategies.
In the GEO profession, AI can help assess a web searcher’s intent, perform keyword research, and optimize a business’ content for better rankings on traditional search engines and AI driven models like ChatGPT and Gemini.
The term “cognitive computing” is used interchangeably with the term artificial intelligence.
Bias
Systematic and undesired preferences or errors in an AI model’s output that can result from skewed or unrepresentative training data. Bias can lead to outputs that are inaccurate, unfair, or perpetuate stereotypes.
Chain-of-Thought Prompting
A prompting technique that encourages a large language model to “think out loud” by providing a series of intermediate steps or a logical reasoning process before arriving at a final answer. This can improve the accuracy of the model’s responses, especially for complex problems.
Chatbot
A program that communicates with humans through a text interface that simulates human language.
Context Window
The maximum amount of text or data (often referred to as “tokens”) that a generative AI model can consider at one time when generating a response. A larger context window allows a model to understand and reference more information from a conversation or document.
Diffusion Model
A type of generative AI model that works by adding noise to a training image and then learning to reverse the process to generate a new, high-quality image from random noise. These models are particularly popular for generating realistic and creative images.
Fine-Tuning
The process of taking a pre-trained generative AI model and training it further on a smaller, specific dataset to specialize its capabilities for a particular task or domain. This allows a model to become more accurate and tailored for a given purpose.
Generative Adversarial Network (GAN):
A generative model architecture composed of two neural networks: a generator that creates new data (e.g., images) and a discriminator that evaluates the authenticity of the generated data. The two networks compete, leading to the creation of increasingly realistic content.
Generative Artificial Intelligence (GenAI)
Generative artificial intelligence, often shortened to generative AI or GenAI, is a subset of artificial intelligence that focuses on creating new and original content such as text, images, music and videos, based upon patterns it learns from existing datasets.
Unlike traditional AI models that are designed to analyze and classify existing data (for example, identifying an object in a photo), generative AI models are trained on massive datasets (often billions or trillions of data points) to learn the underlying patterns, structures, and relationships within the data.
This “learning” process allows them to generate novel and unique content (oftentimes in natural language) that is similar to, but not a copy of, the data they were trained on. The ability of GenAI to understand (and act upon) conversational inputs is a primary reason for its widespread use today.
Note: There are special ethical and social challenges surrounding GenAI, due to its ability to create highly convincing fake content – commonly referred to as “deepfakes.”
Guardrails
Policies and restrictions placed on AI models in order to ensure data is handled responsibly and the models don’t create disturbing or unintended content.
Hallucinations
A phenomenon where a generative AI model, particularly a large language model (LLM) like ChatGPT or Claude, generates a response that is factually incorrect, nonsensical, or makes up information – but presents it as if it were absolutely true.
Large Language Model (LLM)
A type of generative AI model (e.g., Gemini or DeepSeek) trained on a massive amount of text data to understand and generate human-like language, in a conversational manner. LLMs are the foundation for many popular chatbots and text-generation tools.
As of July 2025, LLMs only comprise about 5% of all online search queries, but that number is expected to grow exponentially in the future. Brands should begin to optimize their online content with the aim of being cited by LLMs, as this AI driven technology expands into the future.
Latency
The time delay from when an AI system receives a prompt from a user and actually produces the desired or intended output.
Multimodal
The ability of an AI model to process and generate content across different data types, such as text, images, audio, and video. For example, a multimodal model could take a text prompt and an image as input and generate a new image and a descriptive caption.
Natural Language Processing
A branch of AI that uses machine learning and deep learning to give computers the ability to process and understand human language.
Neural Network
A computational model inspired by the human brain, consisting of interconnected nodes (neurons) arranged in layers. These networks are a fundamental building block of modern AI and are designed to recognize patterns and learn from data.
Parameter
A numerical value within a generative AI model that is learned from the training data. The number of parameters is a key indicator of a model’s size and complexity.
Prompt
The input or instruction given to a generative AI model to guide it in generating a response. Prompting is the primary way users interact with these models.
Prompt Chaining
The ability of AI to use information it has learned or gathered during previous interactions with a user to color or influence its future responses on the same or similar topics.
Prompt Engineering
The art and science of crafting effective prompts to get a generative AI model to produce the desired output. It involves using specific techniques (e.g., follow up questions, placeholder position, memory retention) to guide the model’s behavior and improve the quality of its responses.
Retrieval-Augmented Generation (RAG)
A technique that improves the accuracy and reliability of large language models by giving them access to external data sources. This allows the model to retrieve relevant information from a knowledge base (e.g., Bing or Google indices) and incorporate it into its response, reducing the likelihood of hallucinations.
Temperature
A parameter in a generative AI model that controls the randomness or creativity of its output. A lower temperature (closer to 0) produces more conservative and predictable results, while a higher temperature (closer to 1) leads to more varied and imaginative responses.
Token
A basic unit of text or data that a generative AI model uses to process information. A token can be a single word, a part of a word, or even a punctuation mark. The size of a prompt or a response is often measured in tokens.
Google’s flash models, like Gemini 2.5 Flash for example, have a standard context window of 1 million tokens. This is roughly equivalent to 1,500 pages of text, 700,000 words, or 30,000 lines of code.
Transformer
A type of neural network architecture that has become the foundation for many large language models. The key innovation of transformers is their use of a “self-attention” mechanism, which allows them to weigh the importance of different words in a sequence and understand the context of a sentence more effectively.
Zero-Shot Learning
This occurs when an AI model is tested by being asked to complete a task without the benefit of having been trained on the appropriate data. An example would be to ask an AI model to identify a wild animal like a lion, when it has only been trained on domestic animals like cats.