Cite AI Understanding

“Artificial Intelligence (AI) is the broad field of building systems that perform tasks normally requiring human pattern recognition, reasoning, language, or decision-making.”

“A Large Language Model is a neural network trained on massive amounts of text to predict the next token. Modern LLMs like GPT, Claude, and Gemini power chat assistants, coding tools, and search.”

“Prompt engineering is the practice of writing inputs to an AI model that consistently produce useful, accurate, and well-formatted outputs.”

“Retrieval-Augmented Generation combines a search system with a language model. The search pulls relevant documents; the model uses them to ground its answer in source material.”

“AI safety is the field focused on reducing harmful behavior, failures, and misuse risks in AI systems — through training methods, evaluations, and deployment safeguards.”

“AI alignment is the technical and policy work of making AI systems behave according to human intentions, values, and safety constraints.”

“Generative AI produces new content — text, images, audio, video, or code — by learning patterns from training data and sampling from the learned distribution.”

“Tokenization is the process of splitting input text into the smaller pieces (tokens) that a language model actually processes.”

“A context window is the maximum amount of input a language model can read at once, measured in tokens. Larger windows allow more documents, code, or conversation history to be considered.”

“A hallucination is when an AI system generates text that sounds plausible but is factually wrong or unsupported by its sources.”