AI Glossary
Plain-English definitions of the AI terms you see in the news.
Large language model (LLM)A neural network trained on large amounts of text to predict the next token, which lets it generate and analyze language.TransformerThe neural-network architecture, introduced in 2017, that uses attention to relate every part of an input to every other part.AttentionA mechanism that lets a model weigh how relevant each part of its input is when producing each part of its output.TokenThe unit of text a model reads and writes, often a word or a piece of a word.Context windowThe maximum number of tokens a model can consider at once, including both your input and its output.Fine-tuningFurther training a pretrained model on a smaller, specific dataset to change its behavior or specialize it.RLHFReinforcement learning from human feedback: training a model using human preference ratings of its outputs.Retrieval-augmented generation (RAG)A technique where relevant documents are fetched and added to the model's input so its answer can draw on them.EmbeddingA list of numbers that represents the meaning of text, images or audio so similar items sit close together.HallucinationWhen an AI model states something false or unsupported as if it were true.AI agentA system that uses a model to plan and take multi-step actions with tools, such as browsing, running code or calling APIs.AlignmentThe work of making AI systems pursue the goals and values their developers and users intend.AGIArtificial general intelligence: a hypothetical AI able to match or exceed humans across most cognitive tasks.Diffusion modelA generative model that creates images, audio or video by learning to remove noise step by step.MultimodalAble to take in or produce more than one kind of data, such as text, images, audio and video.Open-weight modelA model whose trained parameters are published so anyone can download and run it, subject to its license.