Multi-step prompts work by breaking down a complex task into a series of smaller, sequential sub-tasks, each guided by its own prompt. The initial prompt sets the overall objective, and subsequent prompts then build upon the AI's previous responses or outputs. This iterative process allows for dynamic refinement, where the user can provide additional context, constraints, or corrections at each stage. Essentially, the output of one step often becomes the input or context for the next, enabling the AI to maintain coherence and accuracy throughout the process. This approach is particularly effective for intricate problems, as it helps the model manage complexity and reduce the likelihood of errors compared to a single, monolithic prompt. It also facilitates the exploration of different facets of a problem systematically, leading to more comprehensive and tailored results. More details: https://info-gpt.top