AI Glossary

An AI system that can autonomously perform tasks, make decisions, and take actions to achieve specific goals.

AI systems capable of independent reasoning, planning, and executing multi-step tasks without constant human guidance.

Overhyped AI systems that claim to predict inherently unpredictable outcomes like crime, job performance, or market movements.

Automatic Speech Recognition - technology that converts spoken audio into text, powering voice assistants and transcription services.

Application Programming Interface - a set of rules that allows different software applications to communicate with each other.

The use of technology to perform tasks with minimal human intervention, improving efficiency and reducing errors.

Systematic errors in AI outputs caused by imbalanced or unrepresentative training data, leading to unfair or inaccurate results.

Content Delivery Network - a network of servers that delivers web content to users based on their geographic location.

An AI-powered conversational interface that can understand and respond to user queries in natural language.

AI technology that enables computers to interpret and understand visual information from images and videos.

The practice of strategically managing an LLM's context window to optimise AI responses. This includes selecting, organising, and formatting the information provided to the model for best results.

The maximum amount of text an AI model can process at once, measured in tokens. Larger windows allow for more context-aware responses.

Customer Relationship Management - software that helps businesses manage interactions with customers and prospects.

Domain Name System - the internet's phonebook that translates domain names into IP addresses.

A numerical representation of text that captures its meaning, used for semantic search and similarity matching.

Teaching an AI to perform a task by providing only a few examples in the prompt, without additional training.

The process of training a pre-existing AI model on specific data to improve its performance for particular tasks.

Generative Engine Optimisation - the practice of optimising content to appear in AI-generated search responses.

When an AI generates confident but factually incorrect or fabricated information that sounds plausible.

Human-in-the-Loop - a design pattern where humans are involved in AI decision-making to provide oversight and handle edge cases.

The process of using a trained AI model to generate predictions or outputs from new input data.

Large Language Model - AI systems trained on vast amounts of text data that can understand and generate human-like text.

Model Context Protocol - an open standard for securely connecting AI assistants to business data and tools.

A trained AI system that processes inputs and generates outputs based on patterns learned from training data.

AI systems that can process and generate multiple types of content including text, images, audio, and video.

A computing system inspired by biological brains, consisting of interconnected nodes that process information in layers.

Natural Language Processing - AI technology that enables computers to understand, interpret, and generate human language.

The practice of writing effective LLM prompts to achieve desired outputs. Includes techniques for structuring instructions, providing examples, and guiding AI behaviour.

A security vulnerability where malicious inputs trick an AI into ignoring its instructions or performing unintended actions.

Retrieval-Augmented Generation - an AI technique that combines information retrieval with text generation for more accurate responses.

Software as a Service - cloud-based software accessed via subscription rather than traditional installation.

Structured data code that helps search engines understand your website content better.

Search Engine Optimisation - the practice of improving website visibility in traditional search engine results.

Security protocols that encrypt data transmitted between websites and users, indicated by HTTPS in URLs.

A parameter that controls AI output randomness. Lower values produce more predictable responses, higher values more creative ones.

A unit of text that AI models process, typically representing a word or part of a word. Pricing is often based on token usage.

The dataset used to teach an AI model patterns and relationships, fundamentally shaping its capabilities and limitations.

The neural network architecture behind modern AI models like GPT and Claude, enabling parallel processing of text sequences.

Text-to-Speech - technology that converts written text into natural-sounding spoken audio, enabling voice interfaces and accessibility features.

A database optimised for storing and querying embeddings, enabling fast semantic search across large datasets.

A search technique that finds semantically similar content by comparing numerical representations (vectors) of meaning, rather than matching keywords.

An automated message sent from one system to another when a specific event occurs, enabling real-time data transfer.

An AI's ability to perform tasks it wasn't explicitly trained for, using only its general knowledge and the task description.