As artificial intelligence becomes a core part of our everyday tools, platforms, and workflows, understanding the essential terminology is more important than ever—whether you’re a business leader, creative professional, or just AI-curious.

Below is a curated glossary of key AI terms, simplified for clarity and accessibility. Use this guide to level up your understanding and confidently navigate the world of AI and machine learning.

1. AGI (Artificial General Intelligence)

AI that can think and reason like a human across multiple domains.

2. CoT (Chain of Thought)

An approach where AI thinks step-by-step to improve reasoning.

3. AI Agents

Autonomous programs that make decisions and take actions independently.

4. AI Wrapper

Tools or code that simplify the way users interact with AI models.

5. AI Alignment

Ensuring AI systems follow human values and intended goals.

6. Fine-tuning

Improving an AI model by training it on specific, targeted data.

7. Hallucination

When an AI generates incorrect or fabricated information.

8. AI Model

A trained system designed to perform specific tasks using data.

9. Chatbot

An AI tool that simulates human conversation.

10. Compute

The processing power required to train and run AI models.

11. Computer Vision

AI that understands and interprets visual content like images or video.

12. Context

Information that AI retains to improve relevance and accuracy in responses.

13. Deep Learning

A type of AI learning that uses layered neural networks.

14. Embedding

Numeric representations of words or data that AI uses for understanding.

15. Explainability

How transparent or understandable an AI decision or output is.

16. Foundation Model

A large, versatile AI model that can be adapted for many tasks.

17. Generative AI

AI that creates content such as text, images, music, or video.

18. GPU (Graphics Processing Unit)

High-speed hardware that accelerates AI computation.

19. Ground Truth

Verified, factual data used to train and evaluate AI models.

20. Inference

When an AI uses learned knowledge to make predictions on new data.

21. LLM (Large Language Model)

A type of AI model trained on massive datasets to understand and generate text.

22. Machine Learning

The broader field of AI focused on systems that improve through experience.

23. MCP (Model Context Protocol)

A standard method for AI models to access external data.

24. NLP (Natural Language Processing)

AI’s ability to understand and interpret human language.

25. Neural Network

A model inspired by the structure of the human brain.

26. Parameters

Internal variables that AI adjusts during training to learn.

27. Prompt Engineering

Designing effective inputs to guide AI toward desired outputs.

28. Reasoning Model

An AI system focused on making logical decisions and inferences.

29. Reinforcement Learning

AI learning based on rewards and penalties from its actions.

30. RAG (Retrieval-Augmented Generation)

Combining search-based data with generated AI responses.

31. Supervised Learning

Training AI on labeled data (with known correct answers).

32. TPU (Tensor Processing Unit)

Google’s specialized chip designed for AI workloads.

33. Tokenization

Breaking down text into smaller units (tokens) for processing.

34. Training

The process of teaching AI by feeding it data and adjusting parameters.

35. Transformer

A powerful AI architecture behind models like GPT and BERT, used for language tasks.

36. Unsupervised Learning

AI learning patterns in data without explicit labels or categories.

37. Vibe Coding

AI-assisted coding using natural language instructions.

38. Weights

Numerical values that shape how AI models make decisions during learning.