AI Glossary

AI Glossary for Work

Clear definitions for the AI concepts professionals need to understand, apply, and govern in day-to-day work.

Workplace AI

1 terms

Workplace AI

AI Fluency

AI fluency is the ability to use AI tools confidently, evaluate their outputs, and apply them safely to real work.

Core Skill

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Core Skill

Prompt Engineering

Prompt engineering is the practice of designing instructions that help AI models produce useful, reliable outputs.

AI Fundamentals

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AI Fundamentals

Large Language Model

A large language model is an AI system trained on large text datasets to understand and generate language.

AI Fundamentals

Tokenization

Tokenization breaks text into smaller units, called tokens, that AI models process.

AI Fundamentals

Machine Learning

Machine learning is a way to build systems that learn patterns from data instead of following only hand-written rules.

AI Architecture

3 terms

AI Architecture

Transformer

A transformer is a neural network architecture that uses attention to process relationships across tokens.

AI Architecture

Retrieval-Augmented Generation

Retrieval-augmented generation, or RAG, gives an AI model relevant source material before it generates an answer.

AI Architecture

Neural Network

A neural network is a machine learning model that learns patterns through connected layers of mathematical units.

Model Adaptation

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Model Adaptation

Fine-Tuning

Fine-tuning is training an existing AI model further so it performs better on a specific task, domain, or style.

Agentic AI

2 terms

Agentic AI

AI Agent

An AI agent is an AI system that can plan steps, use tools, and take actions toward a goal.

Agentic AI

Tool Use

Tool use lets an AI system call external functions, APIs, search systems, calculators, databases, or business applications.

AI Risk

3 terms

AI Risk

Hallucination

A hallucination is an AI output that sounds plausible but is false, unsupported, or not grounded in the provided context.

AI Risk

AI Governance

AI governance is the system of policies, roles, controls, and review processes for responsible AI use.

AI Risk

Bias in AI

Bias in AI is systematic unfairness or skew in model outputs, often caused by data, design, or deployment choices.

Prompting

3 terms

Prompting

Few-Shot Learning

Few-shot learning gives an AI model a small number of examples so it can imitate a pattern.

Prompting

Context Engineering

Context engineering is designing the information, tools, memory, and constraints an AI system needs to perform a task.

Prompting

Structured Output

Structured output is AI-generated content returned in a predictable format such as JSON, tables, lists, or schemas.

AI Data

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AI Data

Embedding

An embedding is a numeric representation of meaning that lets software compare similarity between items.

Generative Media

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Generative Media

Diffusion Model

A diffusion model generates media by learning how to turn noise into structured outputs such as images.

Production AI

2 terms

Production AI

MLOps

MLOps is the set of practices used to deploy, monitor, maintain, and govern machine learning systems.

Production AI

Model Drift

Model drift is performance degradation caused by changes in the data or environment a model sees after deployment.

AI Regulation

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AI Regulation

EU AI Act

The EU AI Act is a European regulation that sets risk-based rules for AI systems and AI literacy obligations.

AI Governance

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AI Governance

Human-in-the-Loop

Human-in-the-loop AI keeps people involved in reviewing, approving, correcting, or escalating AI outputs.

Deployment

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Deployment

Edge AI

Edge AI runs AI models on local devices such as phones, sensors, cameras, vehicles, or factory equipment.

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