AI and Data

AI (artificial intelligence) is software that does tasks that used to need a person, such as writing, sorting or predicting, by learning patterns from data.

Machine learning is the part of AI where software learns rules from examples instead of being told them, then uses those rules to predict or sort new cases.

Generative AI is AI that creates new text, images, audio or code from a request. What it writes is a likely draft built from patterns, not a checked fact.

An LLM (large language model) is AI trained on masses of text to predict the next piece of a sentence. That is how chat tools write answers, right or wrong.

An AI model is the trained file at the heart of an AI tool: it takes an input, applies patterns learned from data, and returns a prediction, a label or text.

A token is a piece of a word that an AI language model reads and writes. Usage is counted and billed in tokens, and the same text takes more in some languages.

A context window is how much text, in tokens, a model can see at once: instructions, files, the chat so far and its reply. What falls outside, it cannot use.

A prompt is the instruction you give an AI tool: the question, the task, the context and the format you want. A clearer prompt gets a more useful answer.

Model training is how an AI model learns: it sees many examples, checks its guesses against the right answers and adjusts itself until the errors shrink.

Inference is the moment a trained AI model does its job: it takes a new question and produces an answer. It happens on every request, so it is the ongoing cost.

An embedding is a list of numbers that stands for the meaning of a text or image, so texts that mean the same land close together, even with different words.

A vector database stores embeddings and finds the items closest in meaning to a question, even when the question and the document use different words.

RAG (retrieval-augmented generation) is a method where AI answers from your documents: it first finds the relevant passages, then writes the answer from them.

An AI hallucination is an answer an AI model makes up and states with confidence, such as an invented price, rule or source, because it predicts likely words.

An AI agent is AI software that does tasks, not just answers: it plans steps and uses connected tools, such as a calendar or email, to reach a goal you set.

Fine-tuning is extra training of a ready AI model on your own examples, so it learns a style, format or task. It changes how it answers, not what it knows.