Why This Glossary Exists
AI moves fast. New terms appear in product announcements, research papers, and developer forums almost every week. If you have ever read a blog post about AI and hit a wall of acronyms - RAG, LoRA, RLHF, embeddings, fine-tuning - you know how quickly the jargon can get in the way of actually understanding what is going on.
This glossary is our attempt to fix that. We define terms in plain language first, then give you the technical context if you want to go deeper. You do not need a machine learning background to use it. We wrote it for product people, marketers, founders, and developers who are working with AI tools without necessarily having studied the underlying research.
We update the glossary regularly as new concepts enter common usage. If you think a term is missing or a definition could be clearer, let us know via the contact page.
Definitions written for humans, not researchers.
New terms added as the field evolves.
214+ terms across every letter.
Useful for beginners and experienced practitioners.
10 terms
Showing all terms starting with M
A branch of AI where algorithms improve automatically through experience and exposure to data, without being explicitly programmed.
A mathematical function trained on data that maps inputs to outputs. In AI, models range from simple regressions to billion-parameter LLMs.
AI models that can process and generate multiple types of data - such as text, images, audio, and video - within a single unified system.
A pre-training objective where random tokens are masked and the model learns to predict them, used in BERT-style encoder models.
A neural network with an external memory component it can read from and write to, enabling longer-term storage than context alone.
Training AI models to learn how to learn, enabling rapid adaptation to new tasks with minimal data, also called "learning to learn".
A structured document describing an AI model's intended use, training data, performance metrics, limitations, and ethical considerations.
A degradation phenomenon where models trained on AI-generated data progressively lose diversity and accuracy over successive generations.
An architecture that routes different inputs to specialised sub-networks (experts), enabling very large models to run efficiently at inference.
A framework where multiple AI agents collaborate, negotiate, or compete to complete complex tasks that exceed the ability of a single agent.
Commonly Misunderstood AI Terms
These are the terms that come up in almost every AI conversation but are often used loosely or incorrectly. Worth knowing what they actually mean.
A type of AI trained on massive amounts of text to predict and generate language. Models like GPT-4, Claude, and Gemini are all LLMs. The "large" refers to the number of parameters - the internal settings the model learned during training.
When an AI generates information that sounds confident and plausible but is factually incorrect. It is not lying - the model genuinely does not know what it does not know. This is one of the most important limitations to understand when using AI-generated content.
The practice of writing inputs to AI models in ways that produce better outputs. A well-structured prompt gives the model context, a clear task, constraints, and sometimes examples. It is less about magic words and more about being precise about what you want.
A technique where an AI retrieves relevant documents or data before generating a response, rather than relying solely on what it learned during training. RAG is how tools like Perplexity AI give you up-to-date answers with citations.
Taking a pre-trained model and training it further on a specific dataset to specialise its behaviour. A fine-tuned model for legal documents will behave differently from the same base model fine-tuned on medical records, even though they started from the same foundation.
A way of representing text, images, or other data as numbers in a high-dimensional space, so that similar things end up close together mathematically. Embeddings power semantic search, recommendation systems, and many RAG implementations.