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 L
A compressed, abstract multi-dimensional representation of data learned by an AI model, where similar items cluster together.
A deep learning model trained on vast text datasets to understand and generate human-like language. Examples include GPT-4, Claude, and Gemini.
A mathematical measure of how far a model's predictions are from the correct answers, used to guide training via gradient descent.
Low-Rank Adaptation - an efficient fine-tuning method that injects small trainable matrices into a frozen pre-trained model, reducing compute costs.
The ground-truth answer or category associated with a training example in supervised learning, used to compute the loss during training.
A probabilistic model trained to predict the next word or token in a sequence, forming the basis for modern text AI systems.
The time delay between sending a request to an AI model and receiving the first token or full response, critical for real-time applications.
A normalisation technique applied within transformer layers that stabilises training by normalising activations across the feature dimension.
A hyperparameter controlling how much model weights are updated per training step - too high causes instability, too low causes slow convergence.
Mechanisms that allow AI agents to persist and retrieve information across multiple sessions, enabling personalisation and continuity.
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.