How does a well-structured prompt reduce complexity of the required output?

A well-structured prompt reduces the complexity of the required output by providing clear and specific instructions to the model. When a prompt is well-structured, it breaks down a complex task into smaller, more manageable components, guiding the model step-by-step towards the desired outcome. This reduces the ambiguity and uncertainty the model faces, allowing it to focus on generating the correct output without having to decipher vague or unclear instructions. A well-structured prompt defines the task, specifies the desired format, provides necessary context, and sets clear constraints, which significantly limits the scope of possible responses. For example, instead of simply asking 'Write a story', a well-structured prompt might ask 'Write a short story about a cat who goes on an adventure in a forest, using a whimsical tone and ending with a moral lesson about friendship.' The added details and constraints make the task more focused and reduce the likelihood of the model generating an irrelevant or off-topic response. By guiding the model with detailed instructions, a well-structured prompt effectively simplifies the task and ensures a more predictable and accurate outcome.