How to reduce repetition and redundant information in generated content through prompt optimization?

When it is necessary to reduce repetition and redundant information in generated content, the core of prompt optimization lies in clarifying content structure, limiting information scope, and setting output constraints. Clarify structural requirements: Specify a content framework in the prompt, such as "Explain in 3 points, each focusing on one core viewpoint and avoiding overlapping expressions", to guide the AI to output content in logical layers and reduce content overlap. Limit information scope: Narrow down the AI's information retrieval range through instructions like "only include the latest data in the XX field" or "exclude explanations of basic concepts", to avoid repeated elaboration of known content due to overly broad coverage. Use restrictive instructions: Add specific requirements such as "avoid repeated words" and "remove redundant examples". For instance, prompt "Express similar viewpoints in different sentence structures, with only 1 case per argument" to force the AI to optimize expression efficiency. It is recommended to first test basic prompts, then supplement structural requirements and constraints based on the generated results to gradually improve the precision of the prompt. Additionally, try using feedback such as "conciseness score" to guide the AI to self-optimize and reduce content redundancy.