Artificial intelligence has exploded into mainstream use, but the terminology can feel overwhelming when you’re just getting started. Whether you’re a student, entrepreneur, or tech-curious professional, understanding the foundational language of AI will help you navigate tools, concepts, and conversations confidently.
This glossary breaks down essential AI terms in clear, beginner-friendly language — no advanced math or coding required.
Artificial Intelligence (AI)
The field of creating computer systems that can perform tasks normally requiring human intelligence, such as learning, problem-solving, pattern recognition, and decision-making.
Machine Learning (ML)
A subset of AI where computers learn from data instead of being explicitly programmed. The system improves its performance over time based on patterns it identifies.
Deep Learning
A type of machine learning that uses neural networks with many layers to analyze complex patterns. This technology powers image recognition, voice assistants, and most modern AI models.
Neural Network
A computer system inspired by the human brain, made up of interconnected nodes (or “neurons”) that process data and learn patterns.
Large Language Model (LLM)
A deep learning model trained on massive amounts of text to understand and generate human-like language. Examples include GPT-based models and other leading generative AI systems.
Generative AI
AI that creates new content — such as text, images, audio, or video — based on the data it has learned from. It doesn’t just analyze data; it generates new material.
Training Data
The information used to teach an AI system how to perform a task. High-quality, diverse training data is essential for accurate results.
Fine-Tuning
A process where a pre-trained AI model is trained further on specific data to specialize in a particular task or industry (e.g., legal or medical AI).
Parameters
Internal settings in an AI model that influence how it learns and makes predictions. Modern models can have billions of parameters.
Prompt
The text or instruction given to an AI model to get a response. Good prompts lead to better results.
Example: “Explain machine learning to a 10-year-old.”
Prompt Engineering
The practice of designing effective prompts to guide an AI system to produce the best possible output.
Tokens
Units of text (words or pieces of words) an AI model processes. Output and pricing for AI tools often depend on token usage.
Hallucination
When an AI confidently produces inaccurate or fabricated information. It’s a known limitation in current systems.
Bias
Unintended or unfair tendency in AI outputs caused by biased training data. Responsible AI practices aim to reduce this.
Dataset
A collection of data used to train or evaluate an AI model. Datasets can include text, images, audio, or structured data.
Computer Vision
A field of AI that enables computers to understand and interpret visual content, such as photos and videos.
Natural Language Processing (NLP)
AI technology that enables machines to understand and generate human language. Used in chatbots, translation, and sentiment analysis.
Reinforcement Learning
A learning method where an AI agent learns by trial and error and receives rewards or penalties based on its performance.
Supervised Learning
A machine learning method where the model is trained on labeled data — meaning the correct answers are provided during training.
Unsupervised Learning
A method where the model learns patterns from unlabeled data — without knowing the correct answers in advance.
AI Agent
A system that can take actions on behalf of a user, make decisions, and operate more autonomously than chat-only AI tools.
Model Training
The process of feeding data to an AI system so it can learn how to perform tasks.
Inference
When an AI model uses what it’s learned to make predictions or generate responses.
Overfitting
When a model learns training data too well, including errors or noise, and performs poorly on new data.
API (Application Programming Interface)
A connection method that allows software to communicate with an AI model — enabling businesses to integrate AI into apps and websites.
Chatbot
An AI system that interacts with users through text or speech. Chatbots can answer questions, automate tasks, and provide customer support.
Ethics in AI
Principles and practices designed to ensure AI systems are fair, transparent, secure, and respectful of human rights.
AI Governance
Policies and procedures that guide safe and responsible AI development and usage within organizations.
Explainable AI (XAI)
AI techniques that make model decisions transparent and understandable to humans — important for trust and accountability.
Edge AI
AI run directly on devices (phones, cameras, IoT) rather than cloud servers. This increases speed and privacy.
Conclusion
Learning AI terms is the first step toward confidently using and evaluating artificial intelligence in your work or business. As the industry evolves, staying familiar with key concepts will help you adopt new tools responsibly and strategically.