Which technique helps guide a model to generalize based on a few examples?

Few-shot prompt engineering is a technique that helps guide a model to generalize from a limited number of examples. This approach is particularly useful in scenarios where obtaining a large dataset is challenging or impractical. By providing a small number of handcrafted examples within the prompt, the model can effectively learn to infer patterns and make predictions based on those limited inputs.

This method leverages the model's ability to adapt and generalize, as it can draw relationships and understand the context from the few examples presented. It is a key concept in natural language processing and machine learning, enabling models to perform tasks even when only limited data is available, thus making it highly effective in real-world applications where data scarcity might be an issue.

In contrast, zero-shot prompt engineering involves making predictions without any task-specific examples, so it lacks the tailored approach that few-shot provides. Continuous learning refers to the ongoing ability of a model to learn from new data while retaining previously learned information, but it does not specifically relate to the concept of generalizing from few examples. One-shot learning, while also about training a model from a single example, is more focused on that singular instance rather than utilizing the broader context of several examples to build understanding.