Tool Use in AI

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.

System Prompt

An initial instruction set provided to a language model that defines its persona, constraints, output format, and behavioural guidelines for a given session or application.

A technique combining information retrieval with text generation, allowing AI to access external knowledge before generating responses.

The branch of ethics examining moral issues surrounding the development, deployment, and impact of artificial intelligence on society.

The simulation of human intelligence processes by computer systems, including learning, reasoning, and self-correction.

A formal representation of knowledge as a set of concepts, categories, and relationships within a specific domain.

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

AI designed to handle specific tasks without possessing self-awareness, consciousness, or true understanding of the task domain.

The frameworks, policies, and regulations that guide the responsible development and deployment of AI technologies.

An inference strategy that starts with a goal and works backward through rules to determine what facts must be true.