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.
8 terms
Showing all terms starting with I
The use of AI models to create new images from text descriptions or other inputs, as seen in tools like Midjourney and DALL-E.
The process of running a trained AI model to produce predictions or outputs from new input data - distinct from the training phase.
The ability of LLMs to adapt to new tasks using only examples or instructions provided in the prompt, without updating model weights.
A computer vision task that assigns a class label to every pixel in an image, used in medical imaging, autonomous driving, and editing tools.
Fine-tuning a language model on a dataset of (instruction, response) pairs to make it better at following diverse natural language instructions.
Using AI to enhance and extend human cognitive capabilities rather than replacing them entirely, combining human judgment with machine speed.
Research into understanding what computations neural networks perform internally, often probing individual neurons or attention patterns.
The integration of AI with Internet of Things sensors and devices to enable real-time intelligent decision-making at the network edge.
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.