In-Context Learning

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.

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.

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.

Empirical relationships describing how AI model performance improves predictably with increases in model size, training data volume, and computational resources.

A statistical approach to AI that uses Bayes' theorem to update probability estimates as new evidence becomes available.

A sequence of data processing and model execution steps that automate the flow from raw data to AI-driven outputs.

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

The interdisciplinary field dedicated to making AI systems safe, robust, and beneficial while minimizing risks of unintended consequences.

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

An approach to AI that uses human-readable symbols and rules to represent problems and derive solutions through logical reasoning.

The ability of an AI system to maintain performance under varying conditions, adversarial attacks, or noisy input data.