Zero-Shot 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.

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.

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.

Google's custom-designed application-specific integrated circuit for accelerating machine learning workloads.

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

When an AI model generates plausible-sounding but factually incorrect or fabricated information with high confidence.

A measure of machine intelligence proposed by Alan Turing, where a machine is deemed intelligent if it can exhibit conversation indistinguishable from a human.

The component of an AI system that applies logical rules to a knowledge base to derive new information or make decisions.

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

An AI algorithm that generates a sequence of actions to achieve a specified goal from an initial state.

The degree to which humans can understand the internal mechanics and reasoning of an AI model's predictions and decisions.