AI Glossary
Browse clear explanations of AI tools, fundamentals, models, and technologies by topic or alphabetically.
Fundamentals62 terms
Core concepts that help explain how AI works.
A
- FundamentalsAGI (Artificial General Intelligence)AGI is the idea of an AI that can handle a wide range of intellectual work at human level, rather than being limited to one narrow task. It is the stated goal of many leading AI companies.Read the explanation
- FundamentalsAI (Artificial Intelligence)AI is an umbrella term for computer technologies that perform tasks associated with human intelligence, such as understanding, prediction, and decision-making.Read the explanation
- FundamentalsAI and CopyrightCopyright questions about AI fall into two separate areas: whether protected works can be used as training data, and who owns AI output and whether it infringes existing works.Read the explanation
- FundamentalsAI BiasAI bias is skew in a model's judgments or output. It usually originates in the training data, and it matters most where decisions are expected to be fair.Read the explanation
- FundamentalsAI CodingAI coding covers any development work done with AI assistance, from inline completion to agents that read a repository, write changes, and run tests.Read the explanation
- FundamentalsAI CreditsAI credits are a service-specific unit that measures how much you have used features such as image generation or AI agents. Paid plans usually include a monthly allowance.Read the explanation
- FundamentalsAI HallucinationAn AI hallucination is a fluent, confident answer that contains invented or incorrect facts. Important names, dates, numbers, and sources require verification.Read the explanation
- FundamentalsAI LiteracyAI literacy is the knowledge and judgment needed to understand what AI is good and bad at, and to use it well. It is increasingly treated as a basic skill rather than a specialty.Read the explanation
- FundamentalsAI MemoryAI memory lets an assistant carry your preferences and past context into new conversations. It is distinct from the context of a single chat and from training the underlying model.Read the explanation
- FundamentalsAI ModelAn AI model learns patterns from large amounts of data and then predicts or generates output from new input. It is the engine inside a product such as ChatGPT.Read the explanation
- FundamentalsAI SearchAI search looks across the web for you, reads multiple sources, and returns an answer written as prose. Most services show the source links alongside the answer.Read the explanation
- FundamentalsAlignmentAlignment is the research and engineering work of making an AI's goals and behavior match human intent and values. It is the central topic in AI safety.Read the explanation
- FundamentalsAPI (Application Programming Interface)An API is a defined way for software to request data or functions from another service. AI APIs let developers add models to their own apps and workflows.Read the explanation
- FundamentalsAPI KeyAn API key is a secret string that identifies which account or project is calling an API, and controls permissions and usage. Treat it with the same care as a password.Read the explanation
- FundamentalsASI (Artificial Superintelligence)ASI describes an AI that would far exceed human intelligence across every field. It is discussed as the stage beyond AGI.Read the explanation
B
- FundamentalsBenchmarkA benchmark is a standardized test used to compare AI models. Scores are a useful reference, not a definitive measure of usefulness for your work.Read the explanation
- FundamentalsBranchA branch is a separate line of development in Git, letting you work on a feature or a fix without disturbing the stable version.Read the explanation
C
- FundamentalsChatbotA chatbot is any program that converses with people through chat. Generative AI changed them from scripted responders into systems that hold a natural conversation.Read the explanation
- FundamentalsCLI (Command Line Interface)A CLI lets you operate a computer by typing commands instead of clicking. You work in a terminal, entering short instructions rather than using buttons and icons.Read the explanation
- FundamentalsCloneCloning copies a repository — its files and its history — from a service such as GitHub onto your own machine so you can start working with it.Read the explanation
- FundamentalsCommitA commit records a selected set of changes into Git's history along with a message describing them. It is saved locally first, and is not sent anywhere until you push.Read the explanation
- FundamentalsContext EngineeringContext engineering is the practice of choosing what information an AI receives — instructions, source material, history, tools, memory — and shaping how it is delivered.Read the explanation
- FundamentalsContext WindowA context window is the amount of conversation, instructions, and source material an AI model can consider at once, measured in tokens.Read the explanation
- FundamentalsCredentialsCredentials are the information used to establish who someone is and whether they may use a service — passwords, API keys, access tokens, and certificates.Read the explanation
- FundamentalsCustom InstructionsCustom instructions store the tone, assumptions, and rules you want an assistant to follow, so they apply across conversations instead of being repeated each time.Read the explanation
D
- FundamentalsDeep LearningDeep learning trains many-layered neural networks modeled loosely on the brain. It is the technique behind almost every AI advance since the 2010s.Read the explanation
- FundamentalsDeepfakeA deepfake is a convincing fake image, video, or audio clip produced with AI. As quality improves, judging authenticity by eye is no longer reliable.Read the explanation
- FundamentalsDeployDeploying puts a site or application onto a server or runtime so that people can actually use it. It is a separate step from pushing code to a repository.Read the explanation
- FundamentalsDesign HarnessA design harness is a working setup that gives an AI the design principles, references, procedure, and review method it needs to produce consistent screens.Read the explanation
F
- FundamentalsFine-tuningFine-tuning trains an existing model further on additional data to adapt it to a specific use, rather than building a model from scratch.Read the explanation
- FundamentalsFoundation ModelA foundation model is a large model pre-trained on vast data and reusable as a base for many purposes. GPT, Gemini, and Claude are the best-known examples.Read the explanation
G
- FundamentalsGenerative AIGenerative AI creates new text, images, audio, video, or code. ChatGPT and modern image generators are familiar examples.Read the explanation
- FundamentalsGPU (Graphics Processing Unit)A GPU is a chip that performs huge numbers of calculations in parallel. Built originally for graphics, it became essential to training and running AI models.Read the explanation
I
K
L
- FundamentalsLLM (Large Language Model)A large language model, or LLM, is an AI model trained on large amounts of text. It is the language-processing engine behind services such as ChatGPT.Read the explanation
- FundamentalsLocal LLMA local LLM runs on your own computer or server instead of a cloud service, so your input never leaves the machine.Read the explanation
M
- FundamentalsMachine LearningMachine learning is the practice of having computers find patterns in data rather than following rules written by hand. Almost all current AI is built this way.Read the explanation
- FundamentalsMarkdownMarkdown is a lightweight plain-text syntax for headings, lists, links, and emphasis. It is widely used in notes, documentation, GitHub, and AI instructions.Read the explanation
- FundamentalsMergeMerging combines the changes from one Git branch into another. When both sides changed the same lines, you get a conflict that a person has to resolve.Read the explanation
- FundamentalsMultimodal AIMultimodal AI can work with more than one type of information, such as text, images, audio, and video, within the same task.Read the explanation
N
O
- FundamentalsOAuthOAuth lets you grant an application limited access to a service without handing over your password. You approve a specific scope, and the app receives a token instead of your credentials.Read the explanation
- FundamentalsOn-device AIOn-device AI runs the processing on your phone or computer rather than in the cloud. It offers privacy, lower latency, and offline use, within the limits of the hardware.Read the explanation
- FundamentalsOpen Source ModelAn open source model is one whose weights are published, so anyone can download, run, and modify it. Because licenses vary, the term open weights is often more accurate.Read the explanation
P
- FundamentalsParameterParameters are the adjustable numbers inside a model. Parameter count is the most common shorthand for how large a model is.Read the explanation
- FundamentalsPre-trainingPre-training is the first stage of building a model, where broad capability is acquired from a very large dataset. The P in GPT stands for it.Read the explanation
- FundamentalsPromptA prompt is the question or instruction given to an AI. Clear goals, context, constraints, and output formats usually produce better results.Read the explanation
- FundamentalsPullPulling fetches new commits from a remote such as GitHub and integrates them into your current local branch.Read the explanation
- FundamentalsPull RequestA pull request proposes merging one branch into another and provides a place to review, discuss, and approve the change before it lands.Read the explanation
- FundamentalsPushPushing sends commits from your local repository to a remote such as GitHub, making them available to everyone else.Read the explanation
R
S
- FundamentalsSecond BrainA second brain is an external, digital store for what you want to remember, organized so you can retrieve and reuse it rather than holding it all in your head.Read the explanation
- FundamentalsSingularityThe singularity is the hypothesized point at which AI surpasses human intelligence and technological progress becomes too fast to predict.Read the explanation
- FundamentalsSpec-Driven DevelopmentSpec-driven development puts a written specification at the center, and has AI and people implement, test, and update documentation against it.Read the explanation
- FundamentalsStagingStaging is choosing which of your working changes go into the next commit. In Git the holding place for that selection is called the staging area, or the index.Read the explanation
- FundamentalsSummarizationSummarization selects the important information from a long text or conversation and restates it briefly. It is one of the most common uses of generative AI.Read the explanation
- FundamentalsSynthetic DataSynthetic data is generated artificially rather than collected from the real world. It addresses shortages of training data, privacy constraints, and rare-case coverage.Read the explanation
T
- FundamentalsTokenA token is a unit an AI model uses to process text. Context limits and most API pricing are measured in tokens rather than words or characters.Read the explanation
- FundamentalsTraining DataTraining data is the material a model learns from. Its quality and coverage largely determine what a model knows and where it is biased.Read the explanation