Detailed Explanation
Few-Shot Learning (or few-shot prompting) involves giving an LLM a small number of examples (usually 2 to 5) demonstrating the desired input and output format before asking it to complete the actual task. This helps 'steer' the model's behavior, tone, and formatting much more effectively than zero-shot prompting, resulting in higher accuracy for specialized tasks.