Fine-Tuning

A security risk in which hidden or malicious text embedded in a document or webpage tricks an AI system into ignoring its real instructions and following the attacker's instead.

The practice of designing, deploying, and using AI in ways that are fair, transparent, secure, and accountable to the people affected by it.

A technique where an AI model first retrieves relevant information from a specific set of documents before generating an answer, instead of relying only on what it was originally trained on.

The number of examples given to a model within a prompt to guide its response; "few-shot" means the model is shown only a handful of examples before producing new output.

A setting on a language model that controls the randomness of its output; a higher temperature produces more varied, less predictable responses, and a lower one produces more consistent, focused ones.

An individual unit of text, such as a word or piece of punctuation, that an AI language model processes; tokenization is the act of breaking text into these units.

The body of data used to train an AI model, shaping what patterns and language it learns to reproduce.