Few-shot prompting is a technique in natural language processing where a model is provided with a small number of examples to learn from, enabling it to generalize and apply that knowledge to new, unseen data. This approach is especially useful in situations where labeled data is scarce, as it allows the model to adapt with minimal input. It’s particularly advantageous for researchers and developers working on machine learning models that need to be trained efficiently with limited data.
