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.

252 terms

Generative AI

Mockup

A mockup is a static, visual representation of a design used to showcase the appearance and layout of a product or webpage before development.

Generative AI

Mode Collapse

Mode collapse is a common problem in generative adversarial networks where the model produces limited, repetitive outputs instead of diverse results.

AI Ethics

Model Cards

Model Cards are standardized documents that provide clear, concise information about machine learning models, including their intended use, performance, and limitations.

Other

Model Checkpoint

Model checkpoint is a saved state of a machine learning model at a specific point during training, enabling recovery and evaluation.

Generative AI

Model Collapse

Model collapse is a phenomenon where a machine learning model, especially in generative tasks, produces limited or repetitive outputs instead of diverse and meaningful results.

Machine Learning

Model Decay

Model decay is the gradual decline in performance of a machine learning model over time due to changes in the data it processes.

MLOps & Infrastructure

Model Deployment

Model deployment is the process of integrating a trained machine learning model into a production environment where it can make real-time decisions or predictions.

MLOps & Infrastructure

Model Development

Model Development is the process of creating, training, and refining a computational or statistical model to solve specific problems or make predictions based on data.

MLOps & Infrastructure

Model Drift

Model drift is the decline in an AI or machine learning model’s performance over time due to changes in data, behavior, or underlying patterns.

Machine Learning

Model Evaluation

Model evaluation is the process of measuring how well a machine learning or AI model performs using predefined metrics and test data.

Other

Model Explainability

Model Explainability is the ability to clearly understand and interpret how an AI or machine learning model makes its decisions.

Other

Model Fine-Tuning

Model fine-tuning is the process of adapting a pre-trained machine learning model to perform better on a specific task by training it further on a specialized dataset.

Other

Model Fusion

Model fusion is the process of combining multiple machine learning models to create a single, more accurate and robust predictive system.

MLOps & Infrastructure

Model Governance

Model Governance is the structured framework for managing, monitoring, and maintaining analytical and AI models to ensure accuracy, compliance, and ethical use.

Other

Model Hosting

Model hosting is the process of deploying and managing machine learning models on servers or cloud platforms to make them accessible for real-time predictions or batch processing.

MLOps & Infrastructure

Model Inference

Model inference is the process of using a trained machine learning model to generate predictions or outputs from new, unseen data.

Other

Model Interpretability

Model interpretability is the ability to explain how a machine learning model makes its decisions in a clear and understandable way.

MLOps & Infrastructure

Model Management

Model Management is the process of organizing, maintaining, and optimizing machine learning models throughout their lifecycle to ensure accuracy, efficiency, and scalability.

MLOps & Infrastructure

Model Monitoring

Model monitoring is the ongoing process of tracking, evaluating, and validating an AI or machine learning model’s performance, behavior, and outputs after it has been deployed.

MLOps & Infrastructure

Model Operations

Model Operations is the process of managing, deploying, and monitoring machine learning models to ensure they perform effectively in real-world applications.

MLOps & Infrastructure

Model Optimization

Model Optimization is the process of improving a machine learning or AI model’s efficiency, accuracy, and speed to perform better in real-world applications.

MLOps & Infrastructure

Model Registry

Model Registry is a centralized repository that stores, manages, and tracks machine learning models throughout their lifecycle.

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.

Read more Read less

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.