Few-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.

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

The coordination and management of multiple AI agents working together to accomplish complex tasks, routing subtasks between specialised agents based on capability and context.

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

A specialised processor designed to accelerate neural network computations in edge devices and mobile platforms.

Machine learning techniques optimised to run on microcontrollers and extremely resource-constrained embedded devices.

A theoretical form of AI that would have consciousness, self-awareness, and the ability to truly understand rather than simulate understanding.

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

Specialised hardware designed to speed up AI computations, including GPUs, TPUs, and custom AI chips.

Machine Learning

A subset of AI that enables systems to automatically learn and improve from experience without being explicitly programmed.