Zero-Shot Prompting

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.

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.

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

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

An automated technique for designing optimal neural network architectures using search algorithms.

The process of teaching an AI model to recognise patterns by exposing it to large datasets and adjusting its parameters.

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

A technique where a smaller, simpler model is trained to replicate the behaviour of a larger, more complex model.

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

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

Language Model

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