Few-Shot Prompting
Also known as: In-Context Learning, Few-Shot Learning
A technique for guiding large language models by providing a small number of examples within the input prompt to demonstrate the desired task or output format. In accessibility applications, few-shot prompting can help AI systems perform context-specific tasks like correcting captioning errors by showing examples from the current conversation, enabling the model to learn domain vocabulary and speaking patterns without requiring fine-tuning or retraining.