AI Basics
Fundamental concepts for understanding artificial intelligence.
Explore AI Basics →AI glossary hub
The plain-English guide to AI terminology
AIExplainer helps readers understand the words behind modern AI — from LLMs and GPT to RAG, embeddings, agents, and MCP. Use this page as a starting point for the most searched concepts and the core topic categories.
Start with the terms people search most
These high-intent queries are often the first step into the wider AI vocabulary. Each entry links to a clear definition and related concepts.
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Core AI topic categories
Machine Learning
Algorithms and methods that enable systems to learn from data.
Explore Machine Learning →Deep Learning
Neural networks with many layers that learn hierarchical representations.
Explore Deep Learning →Natural Language Processing
AI techniques for understanding and generating human language.
Explore Natural Language Processing →Computer Vision
Teaching machines to interpret and understand visual information.
Explore Computer Vision →Large Language Models
AI models trained on vast text data to understand and generate language.
Explore Large Language Models →AI Agents
Autonomous systems that perceive, decide, and act to achieve goals.
Explore AI Agents →Prompt Engineering
The craft of designing effective inputs for AI models.
Explore Prompt Engineering →Reinforcement Learning
Learning through trial and error with rewards and penalties.
Explore Reinforcement Learning →AI Hardware
Specialised chips and infrastructure for running AI workloads.
Explore AI Hardware →AI Companies
Organisations building and deploying artificial intelligence.
Explore AI Companies →AI Models
Specific AI systems, architectures, and model families.
Explore AI Models →Acronyms
Common abbreviations used throughout the AI field.
Explore Acronyms →Ethics & Safety
Responsible development and deployment of AI systems.
Explore Ethics & Safety →Mathematics
Mathematical foundations underlying AI and machine learning.
Explore Mathematics →Programming Frameworks
Software libraries and tools for building AI applications.
Explore Programming Frameworks →Why this glossary exists
Built for readers who want clarity fast
The AI landscape changes quickly, and so does the vocabulary. AIExplainer is designed to help you move from a vague question like “What is RAG?” to a practical understanding with examples, analogies, and links to related terms.
Useful starting points
Featured entries
Popular definitions worth bookmarking
LLM
A type of AI model trained on vast amounts of text to understand and generate human language.
RAG
A technique that combines AI language models with external knowledge retrieval for more accurate answers.
Transformer
The neural network architecture that revolutionised AI by enabling models to process entire sequences at once.
Agent
An AI system that can perceive its environment, make decisions, and take actions to achieve goals autonomously.
GPT
A family of large language models developed by OpenAI that generate human-like text.