The ability of an AI model to perform a task or recognize categories it was not explicitly trained on, without any examples.
Traditional machine learning models need labeled examples for every category they must recognize. Zero-shot learning lets a model handle new tasks or classes using general knowledge and a description of what is required.
For example, a large language model can classify customer reviews as positive, negative, or neutral when simply asked to, without being shown any example reviews first. Similarly, some image models can identify an animal they never saw in training if given a text description that connects to features they learned. Few-shot learning is a related approach in which a handful of examples are included in the prompt. Zero-shot performance is convenient but often less reliable than fine-tuned models for specialized tasks.
Giving a language model a handful of worked examples inside the prompt so it can follow the same pattern for a new input.
Fine-TuningFurther training a pre-trained AI model on a smaller, specific dataset so it performs better on a particular task or style.
Large Language ModelAn AI model trained on vast amounts of text to understand and generate human-like language, code, and other content.
Prompt EngineeringThe practice of designing and refining instructions given to AI models to get more accurate, useful, and reliable outputs.
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