AI Glossary
Your comprehensive guide to artificial intelligence terminology. From basics to advanced concepts.
AI Agent
An autonomous software entity that perceives its environment, makes decisions, and takes actions to achieve specific goals without continuous human oversight.
Artificial Intelligence (AI)
The simulation of human intelligence processes by computer systems, including learning, reasoning, problem-solving, perception, and language understanding.
Chatbot
A software application designed to simulate human conversation through text or voice interactions, typically used for customer service and lead generation.
Conversational AI
AI systems that can engage in natural, human-like dialogue through text or speech, understanding context and intent to provide relevant responses.
Deep Learning
A subset of machine learning that uses neural networks with many layers to learn representations of data with multiple levels of abstraction.
Generative AI
AI systems capable of creating new content — text, images, audio, video, or code — based on patterns learned from training data.
GPT (Generative Pre-trained Transformer)
A type of large language model developed by OpenAI that uses transformer architecture to generate human-like text based on input prompts.
Hallucination (AI)
When an AI model generates information that sounds plausible but is factually incorrect or fabricated, not grounded in its training data.
Large Language Model (LLM)
A type of AI model trained on massive text datasets that can understand and generate human language, perform reasoning, and complete complex tasks.
Machine Learning (ML)
A subset of AI that enables systems to learn and improve from experience without being explicitly programmed, using algorithms to analyze data and make predictions.
Multi-Agent System
A system composed of multiple interacting AI agents that collaborate or compete to solve problems that are difficult or impossible for a single agent.
Natural Language Processing (NLP)
A branch of AI that enables computers to understand, interpret, and generate human language in a way that is both meaningful and useful.
Neural Network
A computing system inspired by biological neural networks in the brain, consisting of interconnected nodes that process information using connectionist approaches.
Prompt Engineering
The practice of designing and optimizing input prompts to guide AI models toward producing desired outputs, a critical skill for working with LLMs.
RAG (Retrieval-Augmented Generation)
An AI technique that combines information retrieval with text generation, allowing models to access external knowledge bases for more accurate, up-to-date responses.
Reinforcement Learning
A machine learning paradigm where an agent learns to make decisions by receiving rewards or penalties based on its actions in an environment.
RPA (Robotic Process Automation)
Technology that uses software robots to automate repetitive, rule-based digital tasks, often integrated with AI for intelligent process automation.
Sentiment Analysis
The use of NLP to identify and extract subjective information from text, determining whether the expressed opinion is positive, negative, or neutral.
Super Agent
An advanced AI system that orchestrates multiple specialized AI agents to complete complex, multi-faceted tasks requiring diverse capabilities and coordination.
Text-to-Speech (TTS)
AI technology that converts written text into spoken audio, enabling voice bots and accessibility features with increasingly natural-sounding voices.
Token
The basic unit of text that AI language models process — roughly equivalent to a word or word fragment. Model capacity is often measured in context window token limits.
Training Data
The dataset used to teach a machine learning model. The quality, quantity, and diversity of training data significantly impacts model performance.
Transformer
A neural network architecture that uses self-attention mechanisms to process sequential data, forming the foundation of most modern LLMs including GPT and BERT.
Vector Database
A specialized database that stores data as high-dimensional vectors, enabling efficient similarity searches used in AI applications like semantic search and RAG.
Voice Bot
An AI-powered application that can understand spoken language and respond with synthesized speech, enabling natural voice-based interactions for customer service and automation.
Workflow Automation
The use of technology to automate sequences of tasks or processes, reducing manual effort and improving efficiency across business operations.
Zero-Shot Learning
The ability of an AI model to perform tasks it has never been explicitly trained on, by leveraging general knowledge and reasoning capabilities.