AI Glossary

• Agent

  An agent is an AI system that can perform tasks on your behalf based on goals or prompts.

• Agentic

  Describes AI systems that act autonomously to achieve goals.

• Anthropomorphism

  The tendency to attribute human traits to non-human things, like AI.

• Artificial General Intelligence (AGI)

  AGI is a hypothetical AI that can think and learn like a human across many domains.

• Artificial Intelligence

  AI is the science of making machines think and act like humans.

• Bias

  Bias in AI means certain groups or ideas are unfairly favored or penalized.

• Chatbot

  A computer program that talks with users, usually in text or voice.

• Computer Vision

  A field of AI focused on helping machines see and understand images and video.

• Deep Learning

  A type of machine learning that uses neural networks to learn complex patterns.

• Embedding

  Embeddings are ways to turn words and concepts into numbers so AI can work with them.

• Fine-Tuning

  Fine-tuning is the process of customizing an AI model for a specific task or audience.

• Generative Adversarial Networks (GANs)

  A type of AI where two models compete to create realistic fake data.

• Generative AI

  AI that can create new content like text, images, or music.

• Guardrails

  Built-in safety rules that limit how AI behaves or what it can generate.

• Hallucination

  A hallucination happens when an AI makes up incorrect or fictional information.

• Inference

  The process of running a trained AI model to make predictions or decisions.

• Large Language Model (LLM)

  LLMs are advanced AI models trained to understand and generate human-like text.

• Machine Learning

  ML is a type of AI that learns from data to make decisions.

• Model Context Protocol (MCP)

  MCP is a framework that helps AI models manage memory, tools, and user context in a structured way.

• Natural Language Processing (NLP)

  NLP is the field of AI focused on understanding and working with human language.

• Neural Network

  A system inspired by the human brain used to recognize patterns and learn.

• Overfitting

  When an AI model learns training data too well and struggles with new data.

• Prompt

  A prompt is the instruction or message you give an AI to get a response.

• Prompt Engineering

  The practice of designing inputs to get better responses from AI models.

• Reinforcement Learning

  A way for AI to learn by trial and error, like how humans learn from experience.

• Retrieval-Augmented Generation (RAG)

  RAG is a method that lets AI pull in real-time data to answer questions more accurately.

• Supervised Learning

  A method where AI learns from labeled data provided by humans.

• Temperature

  A setting that controls how random or focused an AI's responses are.

• Token

  A token is a chunk of text AI uses to read and generate language.

• Training Data

  The information used to teach an AI model how to perform a task.

• Transformer

  The AI architecture powering tools like ChatGPT and Claude.

• Unsupervised Learning

  AI learns patterns from unlabeled data without human-provided answers.

• Vector Database

  A vector database stores data in a format AI can search and understand using embeddings.

• Zero-Shot Learning

  The ability of an AI to handle tasks it wasn't explicitly trained for.