AI Glossary: 3,885 terms, defined

Plain-language definitions of the vocabulary used across machine learning, model architectures, tooling and AI safety. Every entry opens with a one-sentence definition, then explains where the term is used.

3,885 terms

Computer Vision

Content-Based Image Retrieval

Content-Based Image Retrieval is a technology that enables searching and retrieving images from large databases based on their visual content rather than metadata or keywords.

AI Fundamentals

Context

Context is the surrounding information, conditions, or background that gives meaning to data, content, communication, or decisions.

AI Agents & Systems

Context Awareness

Context awareness is the ability of systems or applications to recognize and utilize information about their environment and user situation to provide relevant responses or actions.

AI Fundamentals

Context Engineering

Context engineering is the practice of deliberately designing, structuring, and supplying the right background information so AI systems can generate accurate, relevant, and reliable outputs.

Other

Context Retention

Context retention is the ability of a system or process to remember and utilize previous information to maintain coherence and relevance in ongoing interactions.

Other

Context-Aware AI

Context-Aware AI is artificial intelligence that understands and adapts to the surrounding context to provide relevant and personalized responses or actions.

Other

Context-Aware Code Generation

Context-aware code generation is the process of automatically producing programming code that adapts intelligently based on the surrounding environment, user inputs, or specific application context.

Other

Contextual AI

Contextual AI is artificial intelligence that understands and responds based on the context of information, enabling more relevant and personalized interactions.

Machine Learning

Contextual Bandit

Contextual Bandit is a machine learning algorithm that selects actions based on context to maximize rewards over time.

Other

Continual Learning

Continual Learning is an approach in machine learning where models continuously update and improve by learning from new data over time without forgetting previously acquired knowledge.

MLOps & Infrastructure

Continuous Delivery

Continuous Delivery is a software development practice where code changes are automatically prepared for release to production, ensuring reliable and frequent deployments.

MLOps & Infrastructure

Continuous Integration

Continuous Integration is a software development practice where developers frequently merge their code changes into a central repository to detect and address integration issues early.

MLOps & Infrastructure

Continuous Training

Continuous training is an ongoing learning process where individuals or teams regularly update and enhance their skills to adapt to evolving industry demands and technologies.

Other

Contract Analysis

Contract Analysis is the process of reviewing and interpreting the terms and conditions of a contract to identify risks, obligations, and opportunities.

Other

Contract Analytics

Contract Analytics is the process of using technology to extract, analyze, and manage data from contracts to improve decision-making and compliance.

Other

Contract Automation

Contract automation is the use of software technology to create, manage, and execute contracts efficiently with minimal manual intervention.

Other

Contract Clause Detection

Contract Clause Detection is the process of identifying and extracting specific provisions or terms within legal contracts using automated tools or software.

Other

Contract Clause Extraction

Contract Clause Extraction is the process of automatically identifying and retrieving specific provisions or sections from contracts using technology.

About the TiorAI AI Glossary

The TiorAI AI Glossary is a free reference of 3,885 artificial intelligence terms, each defined in plain English. Every entry opens with a one-sentence definition that stands on its own, then explains where the term is actually used and how it sits next to the ideas around it. There is nothing to sign up for and nothing to download: each definition has its own page and its own URL, so a term you needed once can be linked to and found again.

Three ways to find a term

They combine, and none of them cancels the others. The A to Z strip narrows the index to a single initial, with the # key collecting the entries that begin with a digit or a symbol and that no letter would reach. The category filter cuts the glossary to one of sixteen subject areas, among them Machine Learning, Deep Learning and Neural Networks, Natural Language Processing, Computer Vision, Generative AI, Reinforcement Learning, AI Agents and Systems, MLOps and Infrastructure, Data and Analytics, and AI Ethics. The search field matches names and definitions, so a concept you can only half-name still surfaces. Every combination has its own URL, which means a narrowed view can be bookmarked or sent to a colleague.

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What the definitions cover

The vocabulary of modern AI, at the three depths people arrive needing. The fundamentals a first week runs into — model, parameter, training, inference, overfitting. The architecture behind current systems — transformers, attention, embeddings, diffusion, retrieval-augmented generation. And the operational and governance language that turns up in a procurement review or a policy document — fine-tuning, hallucination, alignment, model drift, bias. Terms are defined as they are used in practice rather than as they appear in a paper abstract.

Definitions that answer first

Each entry leads with the answer. The opening sentence says what the term means without assuming the rest of the page, which is what makes an entry useful to someone who arrived from a search with exactly one question. The explanation underneath is for the reader who wants more than that: what the idea is commonly confused with, what it looks like in practice, and which neighbouring terms are worth reading next. The card in the index above shows the opening of that definition, so you can tell from the results whether an entry is the one you want.

Where the glossary fits

A glossary answers what something means; it is not the place that tells you what to do about it. Where a concept has products behind it, the AI tools directory is where they are listed, and where it has a working instruction behind it, the prompt library is where that lives. Entries link out to those when the link earns its place, and the glossary stays what a glossary is — the definitions, ordered so they can be browsed, and written so a single one can be read on its own.

Using this glossary

How is the glossary ordered?

Alphabetically by term name, which is the order a reference work is browsed in and the order every page of this archive has always used. The sort menu can switch it to reverse alphabetical, or to recently added and recently updated if you want to see what has changed.

What does the letter strip do?

It narrows the index to terms whose name begins with that character, and it keeps whatever else you have set — a category filter, a search, a sort — so you can move between letters without starting again. The # entry collects the terms that begin with a digit or a symbol, which no letter would reach.

Why do some terms have no specific category?

Entries that span several areas, or that predate the current category set, sit under “Other”. It holds a large share of the glossary, so the filter is most useful for narrowing to a named topic rather than for excluding one.

What is in a glossary entry?

A short definition in plain language, then a longer explanation of where the term is used and how it relates to neighbouring concepts. The card above shows the opening of that definition; the entry itself also links to related terms and, where relevant, to the AI tools that implement the idea.

How do I search the glossary?

The field at the top matches term names and definitions, so a concept you can only half-name will usually surface. Search combines with the category filter and the letter strip, and every combination has its own URL, so a narrowed view can be bookmarked or shared.