Few-Shot Prompting

Few-Shot Prompting is a machine learning technique that enables AI models to perform tasks by providing a small number of input-output examples within the prompt itself. This approach leverages the model’s pattern recognition capabilities to understand task requirements and generate appropriate responses based on the demonstrated examples. Typically involving 2-10 examples, few-shot prompting allows models to adapt to new tasks without additional training or fine-tuning. The technique proves particularly effective for complex reasoning tasks, creative writing, code generation, and domain-specific applications where traditional zero-shot prompting may yield inconsistent results. By carefully selecting representative examples that showcase desired output format, style, and logic, practitioners can significantly improve model performance and reduce response variability across diverse use cases.