RAG (Retrieval-Augmented Generation)
A technique where the AI first searches a set of documents, then writes its answer from what it found. It is how assistants quote current information rather than whatever was in their training data.
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A technique where the AI first searches a set of documents, then writes its answer from what it found. It is how assistants quote current information rather than whatever was in their training data.
How well a system can show why it reached a decision. It matters most where the decision affects somebody — a loan, a diagnosis, a job application — and “the model said so” is not an answer.
A search listing with something extra — stars, prices, an image, an FAQ dropdown. Rich results come from structured data, and they take up more of the screen than a plain blue link.
A system that suggests what somebody might want next based on behaviour — the “customers also bought” of an online shop. One of the oldest commercial uses of machine learning.
An older way of marking up facts, written as attributes on the HTML itself rather than in a separate block. Still understood, but JSON-LD is now the safer choice.
A setting that controls how predictable an AI’s output is. Low temperature gives safe, repeatable answers; high temperature gives varied, more surprising ones — useful for ideas, risky for facts.
AI that produces something new — text, images, code, audio — rather than only classifying or predicting. The generative part is why it can write an answer about your business instead of just linking to it.