Few-Shot Prompting

Prompt Engineering

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

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.

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

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

Systematic errors in AI outputs that arise from biased training data, flawed assumptions, or prejudicial algorithm design.

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

The field of AI dedicated to representing information about the world in a form that computer systems can use for reasoning.

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

A thought experiment by John Searle arguing that executing a program cannot give a computer genuine understanding or consciousness.

A computational approach where problems are defined as a set of variables, domains, and constraints that must all be simultaneously satisfied.

Language Model

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