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Field guideA to Z

AI glossary

Clear definitions for 20 AI terms, the jargon and the concepts. This is the vocabulary you need to follow the rest of the site.

A

API
An Application Programming Interface, a way for different software programs to communicate with each other. APIs let developers connect their apps to AI services.
Artificial Intelligence
Software that can perform tasks that normally require human thinking, such as understanding language, recognising images, or making decisions.

C

D

Deep Learning
A type of machine learning that uses neural networks with many layers to learn complex patterns from large amounts of data.

E

Embedding
A way of representing words, sentences, or other data as lists of numbers so that AI models can understand relationships and similarities between them.

F

Fine-Tuning
The process of taking a pre-trained AI model and training it further on a smaller, specific dataset to improve its performance for a particular task.

G

Generative AI
AI that creates new content such as text, images, music, or code, rather than simply analysing or classifying existing data.

H

Hallucination
When an AI model generates information that sounds confident and plausible but is factually incorrect or entirely made up.

L

Large Language Model
An AI model trained on vast amounts of text that can understand and generate human-like language. Most modern AI assistants are built on large language models.

M

Machine Learning
A type of AI where software learns patterns from data instead of being manually programmed with rules.

N

P

Prompt Engineering
The skill of writing clear, effective instructions for AI tools to get the best possible results.

R

Reinforcement Learning
A type of machine learning where an AI agent learns by trying actions and receiving rewards or penalties based on the outcomes.

S

Supervised Learning
A type of machine learning where the model is trained on labelled data, examples where the correct answer is already known.

T

U

Unsupervised Learning
A type of machine learning where the model finds patterns in data without being given labelled examples or correct answers.