Which technique requires a model to provide output based on limited example inputs?

Few-shot prompt engineering is a technique that focuses on training a model to generate outputs with only a small number of example inputs provided. This approach leverages the model's pre-existing knowledge and capabilities to understand and generalize from just a few instances.

In few-shot prompt engineering, prompts are carefully crafted to guide the model on how to respond despite having limited data. This method is beneficial in scenarios where data collection is expensive or impractical, allowing the model to perform tasks effectively without the need for extensive training examples.

The concept contrasts with other techniques such as zero-shot learning, supervised learning, and deep learning, which either require no examples or more substantial datasets for effective training and output generation. By emphasizing the capability to perform well on limited input, few-shot prompt engineering is crucial in making AI models versatile and efficient in real-world applications.