A key goal when working with large language models is to encourage them to review and improve their own outputs. Two common prompting strategies are used to achieve this: 1) directly instructing the model to engage in a more thorough and careful thought process, and 2) providing the model with illustrative examples of both flawed and corrected outputs. Compare and contrast these two strategies, discussing the potential advantages and disadvantages of each approach for improving a model's final response.