AI Terminology List

AI comes with a mountain of jargon that makes everything sound more complicated than it is. This guide breaks down the essential terms, what they actually mean and why they matter. If you want a fast, practical glossary, this covers the core AI terminology clearly.

Artificial Intelligence (AI)

Systems that perform tasks normally requiring human intelligence. It is a broad category, not a single technology.

Machine Learning (ML)

A subset of AI where models learn patterns from data instead of following a manually coded set of rules.

Model

The mathematical structure that processes input and produces output. Think of it as the “brain” of an AI system.

Parameters

The internal values the model adjusts during training to fit patterns in data. More parameters generally means more capacity.

Training

The process of feeding data into a model so it can learn patterns. This is where the “learning” in machine learning actually happens.

Dataset

The information used to train or evaluate a model. Quality matters more than size.

Neural Network

An architecture inspired (loosely) by the brain. It consists of layers of connected nodes that transform data step by step.

Deep Learning

Machine learning using neural networks with many layers. It powers most modern AI breakthroughs.

Tokens

Small pieces of text (words or chunks of words) that models process. Everything you type is broken into tokens.

Prompt

The input you give the model. Good prompting massively affects output quality.

Inference

The model generating an answer once it is already trained. This is the prediction stage.

Fine-Tuning

Training a model on a smaller, specialised dataset to adjust it for a specific purpose.

Reinforcement Learning

A training method where a model learns by receiving rewards or penalties based on its outputs.

Embeddings

Numeric vectors that represent the meaning of text. Used for search, recommendations and similarity checks.

Latency

The time it takes for AI to return an output. Lower latency means faster responses.

Hallucination

When the model produces confident but incorrect information. A side effect of prediction without understanding.

Context Window

The maximum amount of text the model can consider at once. Larger windows allow deeper reasoning.

AGI (Artificial General Intelligence)

A hypothetical AI that can reason, understand and learn like a human. Does not exist today.

Alignment

Ensuring AI systems behave safely, predictably and in line with human values.

API

A way for developers to connect their apps to an AI model through code.

Final Takeaway

Understanding AI terms helps you work with AI instead of fighting it. This glossary covers the essential language of the field so you can read documentation, use models effectively and understand what is actually happening under the hood.