From LLMs to hallucinations, here’s a simple guide to common AI terms

The rapid advancement of artificial intelligence has introduced numerous technical terms and jargon that have become commonplace in both academic and popular discourse. This glossary serves to demystify key AI concepts that frequently appear in news coverage and public discussions, making the technology more accessible to general audiences who may encounter these terms without understanding their precise meanings.

Core concepts covered in such glossaries typically include foundational terms like Large Language Models, which are neural networks trained on vast amounts of text data to generate human-like responses, and the phenomenon of "hallucinations," where AI systems confidently produce false or fabricated information. Other essential vocabulary includes machine learning, neural networks, transformers, and training data, each representing critical components of how modern AI systems function and their limitations.

Understanding this terminology matters because it enables the public to engage more meaningfully with ongoing debates about AI capabilities, limitations, and implications. As artificial intelligence increasingly influences business decisions, policy-making, and daily life, a shared vocabulary allows stakeholders to communicate precisely about both the promise and risks of these technologies, rather than relying on vague or sensationalized descriptions.