Techniques

Zero-Shot Learning

Définition

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.

Termes Connexes

Few-Shot Learning

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.

Foundation Model

A large AI model trained on broad data at scale that can be adapted to a wide range of downstream tasks through fine-tuning or prompting. GPT-4, Claude, and Gemini are examples; under the EU AI Act, providers of GPAI models face specific transparency obligations.

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.

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