AI Glossary
Clear explanations of RAG, agents, MoE, LoRA and other AI terms: a one-line definition, a plain-language explanation, an example and related tools.
50 terms
AI Agent
An AI system that perceives a goal, plans steps, calls tools and executes tasks.
Context Window
The maximum number of tokens a model can read and remember at once, including input and output.
Diffusion Model
A generative model that turns random noise into images, video or audio through iterative denoising.
Hallucination
When a model produces plausible but factually wrong or unsupported content.
Large Language Model
A model trained on massive text to understand and generate natural language.
Model Context Protocol
An open standard that lets AI applications connect to external tools and data sources.
Open-Weight Model
A model whose weights can be downloaded, deployed locally and modified.
Prompt Engineering
Designing instructions, examples and constraints so a model reliably produces the desired output.
Retrieval-Augmented Generation
Retrieve relevant passages from a knowledge base, then have the model answer from them.
Text-to-Video
Generating video clips from text descriptions.
Token
The smallest unit of text a model processes, often a word, subword or character group.
Vibe Coding
Programming by describing intent in natural language and letting AI generate and modify code.
AI Upscaling
Using models to increase image resolution and add detail.
Agentic Workflow
Breaking a task into steps planned by AI, executed by tools and reviewed by humans.
Artificial General Intelligence
General AI that matches or exceeds humans across a wide range of tasks and can transfer learning.
Automatic Speech Recognition
Converting speech into text.
Benchmark
A standardized dataset and metric for comparing model capabilities.
Brain-Computer Interface
Technology that reads or stimulates brain signals to interact with external devices.
Chain of Thought
Prompting a model to write intermediate reasoning steps before concluding.
ControlNet
An add-on that gives diffusion models structural control from pose, edges, depth and more.
Digital Human
An AI-generated or driven virtual person that can speak and move.
Direct Preference Optimization
An alignment method that optimizes directly on preference data without a separate reward model.
Embedding
Converting text, images or other content into vectors whose similarity can be computed.
Embodied AI
AI that perceives, decides and acts through a physical body.
Fine-tuning
Continue training a model on task data to better match a style or task.
Function Calling
A model selects and calls external functions based on user intent, then uses the result in its answer.
Guardrails
Rules and systems that constrain model inputs, outputs and actions.
Image-to-Video
Generating motion video starting from a still image.
Inference
The computation a trained model performs to produce output from an input.
Inpainting
Repainting only a masked region of an image while keeping the rest.
Latency
The time from sending a request to receiving the model response.
Latent Space
A compressed high-dimensional representation space where diffusion often operates.
Low-Rank Adaptation
Freezing the base model and training a small low-rank adapter for a new task.
Mixture of Experts
Splitting a model into expert subnetworks and activating only a subset per input.
Model Distillation
Training a smaller student model on a larger teacher model's outputs to compress capability.
Model Evaluation
Designing test sets and scoring rules to decide whether a model works for a specific task.
Multi-Agent
Multiple agents collaborating with divided roles to complete complex tasks.
Multimodal
A model that can process or generate multiple modalities such as text, images, audio and video.
Optical Character Recognition
Converting text in images or scans into editable text.
Outpainting
Generating new content beyond the original image boundary.
Quantization
Representing model weights at lower precision to reduce memory and inference cost.
RAG vs Fine-tuning
RAG handles changing knowledge; fine-tuning handles behavior and style customization.
Reinforcement Learning from Human Feedback
Train a reward model from human preferences, then optimize model outputs with reinforcement learning.
Temperature
A parameter controlling output randomness; higher values produce more diverse results.
Text-to-Speech
Converting text into natural-sounding speech.
Tool Use
A model using external tools such as search, code execution or APIs during reasoning.
Top-p Sampling
Sample only from the smallest set of tokens whose cumulative probability reaches p.
Vector Database
A database built to store vectors and retrieve them by similarity.
Voice Cloning
Replicating a person's timbre and speaking style from a small sample.
World Model
An internal representation that learns how an environment works and predicts the consequences of actions.