A developer uses an example-based prompt modification for a multi-step problem-solving task. Which advanced prompting technique are they using?

The advanced prompting technique being used in this scenario is Chain-of-Thought (CoT) Prompting. This technique involves breaking down complex problems into a series of logical steps, allowing the model to reason through the problem systematically. By using example-based prompt modification, the developer is likely providing examples that illustrate each step of the multi-step task, guiding the model to generate coherent and sequential reasoning.

Chain-of-Thought Prompting enhances the model's ability to tackle multi-step problems by making the reasoning process explicit. This is particularly useful in scenarios where understanding the process is as important as arriving at the final answer, as it allows the model to demonstrate its reasoning path, leading to more accurate outcomes.

The other techniques mentioned do not directly relate to the specific approach of modifying prompts based on examples. Few-Shot Learning typically involves providing a limited number of examples to train the model on a new task but does not inherently focus on the reasoning process. Zero-Shot Learning refers to the model's capability to perform a task without any prior examples, and Prompt Engineering pertains more broadly to the design and structuring of prompts without necessarily following the multi-step reasoning approach characteristic of Chain-of-Thought.