AI Glossary

The simulation of human intelligence by machines — enabling computers to learn, reason, and make decisions.

An AI system that can perceive its environment, make decisions, use tools, and take autonomous actions to achieve a goal.

The maximum amount of text (measured in tokens) that an AI model can process in a single interaction.

A subset of machine learning using multi-layered neural networks to learn complex patterns from large datasets.

An AI model that generates images or other media by learning to reverse a gradual noise-adding process.

A numerical vector representation of text, images, or other data that captures semantic meaning in a high-dimensional space.

Training a pre-trained model further on a smaller, task-specific dataset to adapt it for a particular use case.

AI that can create new content — text, images, audio, video, and code — rather than just classifying or predicting.

When an AI model generates confident-sounding but factually incorrect or fabricated information.

A massive AI model trained on text data that can generate, summarize, translate, and reason about language.

A subset of AI where systems learn from data to improve performance without being explicitly programmed.

AI systems that can process and generate multiple types of data — such as text, images, audio, and video — in a unified model.

A computational model loosely inspired by the brain, made of interconnected nodes (neurons) that process information in layers.

The practice of crafting inputs to AI models to elicit better, more accurate, or more useful outputs.

A technique that enhances LLM outputs by first retrieving relevant documents from an external knowledge base before generating a response.

Reinforcement Learning from Human Feedback — a training technique that aligns AI models with human preferences using human ratings.

The neural network architecture behind most modern AI — uses attention mechanisms to process sequences in parallel.

The process of splitting text into smaller units (tokens) that a language model can process.

A parameter that controls the randomness of an AI model's outputs — lower values are more deterministic, higher values are more creative.

A database optimized for storing and querying high-dimensional vector embeddings at scale.