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.
13 terms
Showing all terms starting with A
The simulation of human intelligence in machines programmed to think, learn, and problem-solve autonomously.
An autonomous AI system that plans and executes multi-step tasks by combining reasoning, tool use (web search, code execution, APIs), and memory.
The challenge of ensuring AI systems act in accordance with human values, intentions, and goals rather than pursuing unintended objectives.
A neural network component that allows models to focus on the most relevant parts of an input when producing an output, foundational to transformers.
Automated Machine Learning - tools that automate the process of selecting, training, and tuning ML models without requiring deep expertise.
A model that generates outputs one token at a time, with each token conditioned on all previously generated tokens.
A machine learning approach where the model identifies and requests labels for the most informative data points, reducing the total annotation effort.
A technique that deliberately crafts inputs to fool AI models into making incorrect predictions, exposing vulnerabilities in model robustness.
AI systems capable of taking sequences of actions autonomously to complete long-horizon goals, often using tools, APIs, and memory.
A service that acts as the entry point for AI API calls, handling authentication, rate limiting, routing, and logging for model inference.
A hypothetical AI system with human-level cognitive abilities across all intellectual tasks, as opposed to narrow AI built for specific domains.
An NLP technique that identifies sentiment toward specific aspects or features of a subject, rather than assigning a single overall sentiment score.
AI systems combining computer vision, sensor fusion, and path planning to enable self-driving cars and robots to navigate real-world environments.
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.