Techniques

Few-Shot Learning

Définition

A prompting or training approach where a model learns to perform a new task from only a small number of labelled examples. In-context few-shot prompting lets practitioners shape LLM behaviour without any weight updates, dramatically lowering the cost of customisation.

Termes Connexes

Zero-Shot Learning

The ability of a model to perform a task it has never been explicitly trained or prompted on, relying solely on general knowledge acquired during pre-training. Zero-shot capability is a hallmark of large foundation models and simplifies rapid prototyping.

Prompt Engineering

The practice of designing and optimising input prompts to elicit the best results from AI models, especially LLMs. Structured prompting—using system instructions, examples, and constraints—can dramatically improve output quality without changing the underlying model.

Large Language Model (LLM)

AI models trained on vast amounts of text data that can understand and generate human-like text. Examples include GPT-4, Claude, and Llama. LLMs power modern chatbots, content generation, and code assistance tools.

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