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AI Concepts15
Plain-English explanations of the terms you keep encountering in the news.
Today's concept
Foundation Model
A large AI model trained on vast amounts of general data, designed to be the starting point for many different applications rather than built for a single task.
Agentic AI
AI that can take sequences of actions on its own to complete a goal - planning, using tools, checking its own work, and iterating without needing a human to guide every step.
Context Window
The maximum amount of text an AI can read and think about at once - everything you send it, plus the conversation history, has to fit within this limit.
All concepts
M
MCP (Model Context Protocol)
An open standard that lets AI models connect to external tools and data sources in a consistent way - like a universal plug that makes any AI work with any tool.
Multimodal AI
An AI system that can work with more than just text - handling images, audio, and video alongside written language, and reasoning across all of them together.
R
RAG (Retrieval-Augmented Generation)
A way of making AI smarter by letting it look things up before answering, instead of relying only on what it memorised during training.
RLHF (Reinforcement Learning from Human Feedback)
A training technique that teaches AI to produce responses humans actually prefer, by having real people rate different outputs and using those ratings to improve the model.
T
Token
The basic unit of text that AI models actually process - roughly a word or part of a word, and also the unit used to measure cost and limits when using AI.
Transformer
The AI architecture that powers virtually every major language model today - the underlying design that makes GPT, Claude, Gemini, and most other modern AI systems work.