System Prompt

Prompt Engineering

The practice of designing and optimising input prompts to elicit desired outputs from large language models.

Few-Shot Prompting

A technique where a language model is given a small number of examples within the prompt to guide its response pattern.

Zero-Shot Prompting

Querying a language model to perform a task it was not explicitly trained on, without providing any examples in the prompt.

Chain-of-Thought Prompting

A prompting technique that encourages language models to break down reasoning into intermediate steps before providing an answer.

In-Context Learning

The ability of large language models to learn new tasks from examples provided within the input prompt without parameter updates.

Few-Shot Learning

A machine learning approach where models learn to perform tasks from only a small number of labelled examples, often achieved through in-context learning in large language models.

Zero-Shot Learning

The ability of AI models to perform tasks they were not explicitly trained on, using generalised knowledge and instruction-following capabilities.

Emergent Capabilities

Abilities that appear in large language models at certain scale thresholds that were not present in smaller versions, such as in-context learning and complex reasoning.

Tool Use in AI

The capability of AI agents to invoke external tools, APIs, databases, and software applications to accomplish tasks beyond the model's intrinsic knowledge and abilities.

A table used to evaluate classification model performance by comparing predicted classifications against actual classifications.

A graphical plot illustrating the diagnostic ability of a binary classifier as its discrimination threshold is varied.

A documentation framework that provides standardised information about an AI model's intended use, performance characteristics, limitations, and ethical considerations.

Computing systems that simulate human thought processes using self-learning algorithms, data mining, pattern recognition, and natural language processing.

The research field focused on ensuring AI systems act in accordance with human values, intentions, and ethical principles.

The process of using a trained AI model to make predictions or decisions on new, unseen data.

A method of problem-solving that represents all possible states of a system and searches for a path from initial to goal state.

A measurement of how well a probability model predicts a sample, commonly used to evaluate language model performance.

Language Model

A probabilistic model that assigns probabilities to sequences of words, enabling prediction of the next word in a sequence.