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.
12 terms
Showing all terms starting with T
A sampling parameter that controls the randomness of LLM outputs. Higher values produce more creative responses; lower values are more deterministic.
AI that generates visual images from natural language text descriptions, as in Midjourney, DALL-E, and Stable Diffusion.
AI technology that converts written text into natural-sounding spoken audio, used in voice assistants, audiobooks, and accessibility tools.
The units of text (roughly 4 characters or 3/4 of a word in English) that LLMs process. Costs and context limits are measured in tokens.
Using knowledge gained from training on one task or domain and applying it to a different but related task, reducing the need for large datasets.
The neural network architecture behind most modern LLMs, using attention mechanisms to process sequential data with high parallelism.
An NLP task that assigns predefined categories to text documents, used in spam filtering, sentiment analysis, and topic labelling.
An NLP task that produces a shorter version of a document while preserving its key information, either extractive or abstractive.
The number of tokens or requests an AI inference system can process per unit of time, a key metric for scaling production deployments.
The ability of an LLM to call external functions, APIs, or services during generation, enabling real-time data retrieval and action execution.
A sampling method that restricts token selection to the smallest set whose cumulative probability exceeds P, balancing diversity and coherence.
The dataset used to optimise a model's parameters during the learning phase, directly shaping its capabilities and potential biases.
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.