AI Glossary

Agentic AI refers to AI systems designed to act autonomously toward goals — planning, using tools, and taking sequences of actions rather than just responding to single prompts.

An AI agent is a system that can take a goal, break it into steps, and complete those steps autonomously using AI and connected tools.

AI alignment is the challenge of ensuring that AI systems pursue goals and produce behaviors that match human intentions and values — not just technically, but in practice.

An AI assistant is a software application that uses artificial intelligence to understand natural language, answer questions, and help users complete tasks through conversation.

AI automation uses artificial intelligence to perform repetitive or complex tasks automatically, reducing the need for manual human effort. It goes beyond traditional automation by handling unstructured data and making judgment calls.

An AI copilot is an AI assistant integrated directly into a tool or application to help users complete tasks within that tool — like GitHub Copilot for code or Microsoft Copilot in Office.

AI ethics is the study and practice of ensuring AI is developed and used in ways that are fair, transparent, accountable, and beneficial — minimizing harm and respecting human rights.

AI integration means connecting AI capabilities into existing software, workflows, or business processes — adding AI features to tools you already use rather than replacing them.

AI literacy is the ability to understand, evaluate, and effectively use AI tools — knowing what AI can and can't do, how it works at a basic level, and how to use it responsibly.

AI orchestration is the process of coordinating multiple AI models, tools, and steps into a unified workflow or pipeline to complete complex tasks.

AI safety is the field of research focused on ensuring that AI systems behave as intended, remain reliable, and don't cause unintended harm — now and as AI becomes more capable.

An AI workflow is a repeatable, step-by-step process that uses AI tools to complete a task more efficiently than doing it manually.

An API (Application Programming Interface) is a way for software systems to communicate with each other. AI APIs let developers access AI capabilities from companies like OpenAI and Anthropic in their own applications.

Artificial intelligence is the field of computer science focused on building systems that can perform tasks that normally require human intelligence — like understanding language, recognizing images, and making decisions.

An autonomous agent is an AI system that can independently plan and execute multi-step tasks, making decisions and taking actions without requiring human input at each step.

AI bias refers to systematic errors in AI outputs that favor or disadvantage certain groups, often because the training data reflected existing human biases.

Chain-of-thought prompting asks an AI to show its reasoning step by step before giving a final answer, which significantly improves accuracy on complex or multi-step problems.

A chatbot is a software program that simulates conversation with users, typically to answer questions or handle common tasks. Modern AI chatbots use LLMs; older ones follow scripted rules.

Computer vision is the field of AI focused on enabling computers to interpret and understand images and video — recognizing objects, faces, scenes, and visual patterns.

A context window is the maximum amount of text an AI model can process and remember in a single conversation or session.

Deep learning is a type of machine learning that uses multi-layered neural networks to learn complex patterns. It's the technology behind most modern AI breakthroughs.

A diffusion model is the type of AI behind most modern image generators. It learns to generate images by starting with random noise and progressively refining it into a coherent output.

An embedding is a numerical representation of text (or other data) that captures its meaning. Similar words or sentences have embeddings that are close together in mathematical space.

Few-shot prompting is a technique where you give an AI model a few examples of the input and output you want before asking it to complete your actual task.

Fine-tuning is the process of taking a pre-trained AI model and training it further on a smaller, specific dataset to specialize it for a particular task or style.

A foundation model is a large AI model trained on broad data that can be adapted to many different tasks. GPT-4, Claude, and Gemini are all foundation models.

Function calling is a feature that lets AI models trigger specific functions or APIs in your code, enabling them to take real-world actions beyond generating text.

Generative AI refers to AI systems that can create new content — text, images, audio, video, or code — rather than just analyzing or classifying existing content.

GPT (Generative Pre-trained Transformer) is the AI architecture behind ChatGPT. Developed by OpenAI, GPT models are some of the most widely used large language models in the world.

Grounding in AI means connecting a model's outputs to real, verifiable sources of information — reducing hallucinations and making responses more factually reliable.

Guardrails are safety controls built into AI systems that prevent the model from producing harmful, inappropriate, or off-policy outputs.

AI hallucination is when an AI model generates information that sounds confident and plausible but is factually incorrect or completely made up.

Human-in-the-loop (HITL) is an AI design principle where humans review, approve, or correct AI outputs at key decision points, rather than letting AI act fully autonomously.

Inference is the process of using a trained AI model to generate outputs — when ChatGPT responds to your message, it's running inference.

A knowledge base is a structured collection of information — documents, FAQs, policies, and data — that an AI can be connected to so it can answer questions based on your specific content.

An LLM (Large Language Model) is the AI technology behind tools like ChatGPT, Claude, and Gemini. It's trained on large amounts of text to generate and understand language.

Machine learning is a type of AI where systems learn from data rather than being programmed with explicit rules. The more data they see, the better they get.

Multimodal AI refers to AI systems that can understand and generate multiple types of content — text, images, audio, and video — not just text.

Natural language processing (NLP) is the field of AI focused on enabling computers to understand, interpret, and generate human language.

A neural network is a type of AI model loosely inspired by the human brain, made up of layers of interconnected nodes that learn to recognize patterns in data.

No-code AI refers to tools and platforms that let users build AI-powered applications and automations without writing any code — using visual interfaces and pre-built components.

Open-source AI refers to AI models whose code and weights are publicly available for anyone to download, use, modify, and build upon — in contrast to closed, proprietary models.

Prompt engineering is the practice of writing clear, specific instructions that help AI tools produce better, more useful outputs.

Prompt injection is a security attack where malicious instructions are embedded in content an AI processes, attempting to hijack the AI's behavior or bypass its instructions.

RAG (Retrieval-Augmented Generation) is a technique that connects an AI model to external data sources so it can answer questions based on specific documents or databases.

Reinforcement learning is a type of machine learning where an AI learns through trial and error by receiving rewards for good actions. RLHF (Reinforcement Learning from Human Feedback) is how most modern AI assistants are trained to be helpful.

Speech-to-text AI converts spoken audio into written text. Also called transcription or automatic speech recognition (ASR), it powers voice assistants, meeting transcription, and dictation tools.

A system prompt is a special instruction given to an AI model before the conversation starts, setting its role, behavior, and rules for the entire session.

Temperature is a setting that controls how random or creative an AI's responses are. Low temperature produces focused, predictable outputs. High temperature produces more varied and creative ones.

Text-to-image AI generates visual images from text descriptions. You describe what you want in words, and the AI creates an image matching your description.

Text-to-speech AI converts written text into spoken audio. Modern AI TTS systems produce voices that are nearly indistinguishable from human speech.

A token is the basic unit of text that AI language models process. Tokens are roughly equivalent to word fragments — about 3–4 characters each on average in English.

Tool use refers to the ability of AI models to call external tools — like web search, calculators, code execution, or APIs — to complete tasks beyond pure text generation.

Training data is the dataset used to teach an AI model. The quality and diversity of training data heavily determines what the model knows and how well it performs.

The transformer is the neural network architecture that powers almost all modern AI language models, including GPT, Claude, and Gemini. It was introduced in a landmark 2017 paper.

A vector database stores data as numerical embeddings and allows fast similarity searches — finding the content most semantically similar to a query. Essential for RAG and AI search.

Zero-shot prompting means asking an AI to complete a task without giving it any examples first. The model relies entirely on its training to understand what you want.