Which AI technique helps a model break down complex tasks into smaller, more manageable reasoning steps?

The technique known as Chain-of-Thought (CoT) prompting is particularly effective in helping a model handle complex tasks by breaking them down into smaller, more manageable reasoning steps. This approach involves encouraging the model to verbalize its reasoning process, allowing it to think through the problem systematically rather than attempting to arrive at a final answer in one leap.

By utilizing CoT prompting, models can follow a sequence of logical steps, which enhances their ability to tackle tasks that require nuanced and sequential reasoning. This method improves performance on tasks that may initially seem daunting because it fosters clarity in problem-solving, guiding the model through each stage of reasoning.

In contrast, the other techniques, while beneficial in their own ways, do not directly focus on breaking down tasks in this specific manner. Direct prompting typically provides straightforward instructions without encouraging an exploratory reasoning process. Multi-Task Learning enhances a model's capability by training it on various tasks simultaneously, but it does not specifically address the stepwise reasoning approach. Transfer Learning allows models to apply knowledge gained from one task to another, yet it does not inherently provide a structured method for dissecting complex tasks into simpler components.