# AI glossary

Plain-English definitions of common AI terms, from the Global AI Product Index.

Canonical URL: https://globalaiproductindex.com/glossary/

**AI agent** — A system that uses a model to plan and carry out multi-step tasks, often by calling tools.

**Alignment** — The effort to make AI systems behave according to human intentions and values.

**Attention** — A mechanism that lets a model weigh which parts of the input matter most for each output.

**Chain-of-thought** — A prompting or training approach in which a model works through intermediate reasoning steps before answering.

**Context window** — The maximum amount of text, measured in tokens, that a model can consider at once.

**Diffusion model** — A generative model that produces images or other media by iteratively removing noise from a random signal.

**Distillation** — Training a smaller model to imitate a larger one, keeping much of its capability at lower cost.

**Embedding** — A numerical vector representation of text or other data that captures aspects of its meaning.

**Few-shot learning** — Guiding a model with a handful of examples in the prompt instead of retraining it.

**Fine-tuning** — Further training of a pretrained model on a narrower dataset to adapt it to a specific task or domain.

**Foundation model** — A large model trained on broad data that can be adapted to many downstream tasks.

**Function calling** — A model capability that returns structured calls to developer-defined functions instead of plain text.

**Grounding** — Connecting a model's responses to verifiable external sources such as documents or search results.

**Guardrails** — Rules and filters that constrain an AI system's outputs to keep them safe, accurate, and on-topic.

**Hallucination** — When a model generates text that is fluent but factually incorrect or unsupported.

**Inference** — Running a trained model to produce outputs, as opposed to training it.

**Large language model (LLM)** — A neural network trained on large amounts of text to predict and generate language.

**Mixture of experts (MoE)** — A model architecture that routes each input to a subset of specialized subnetworks to save computation.

**Model Context Protocol (MCP)** — An open protocol that standardizes how AI assistants connect to external data sources and tools.

**Multimodal** — Describing a model that can process or generate more than one type of data, such as text and images.

**Open weights** — Model parameters that are publicly released so others can run or adapt the model.

**Prompt** — The text or other input given to a model to instruct or elicit a response.

**Prompt engineering** — The practice of designing and refining prompts to get more reliable or useful model outputs.

**Quantization** — Reducing the numerical precision of a model's weights so it runs faster and uses less memory.

**Reasoning model** — A model trained to spend extra computation thinking through a problem step by step before answering.

**Red-teaming** — Deliberately probing an AI system for harmful, unsafe, or unintended behavior before and after release.

**Reinforcement learning from human feedback (RLHF)** — A training method that uses human preference judgments to steer a model toward more helpful behavior.

**Retrieval-augmented generation (RAG)** — A technique that retrieves relevant documents and supplies them to a model to ground its responses.

**Small language model (SLM)** — A compact language model designed to run efficiently on devices or at low cost with competitive quality.

**Speech recognition (ASR)** — Automatically converting spoken audio into written text.

**Synthetic data** — Artificially generated data used to train or evaluate models when real data is scarce or sensitive.

**System prompt** — A hidden instruction that sets a model's role, rules, and behavior before user input.

**Temperature** — A sampling setting that controls how random or predictable a model's outputs are.

**Text-to-image** — Generating images from written descriptions using a generative model.

**Text-to-video** — Generating video clips from natural-language descriptions using generative models.

**Token** — A unit of text, such as a word or word-piece, that a language model reads and generates.

**Tool use (function calling)** — A model's ability to call external functions, APIs, or apps to complete a task.

**Transformer** — A neural network architecture based on attention that underlies most modern language and multimodal models.

**Vector database** — A database that stores embeddings and finds items by similarity, often used for retrieval in AI apps.

**Vibe coding** — A style of software development where developers describe goals in natural language and let AI generate the code.

**World model** — An AI system that learns an internal representation of an environment to predict how it changes over time.

**Zero-shot learning** — A model's ability to perform a task without having seen any examples of it during prompting.

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Data from the [Global AI Product Index](https://globalaiproductindex.com/), licensed CC BY 4.0.