How to balance conciseness and information completeness in prompt design?

In prompt design, balancing conciseness and information completeness needs to be dynamically adjusted based on task objectives and AI model characteristics: when rapid execution of clear instructions is required, conciseness takes priority to avoid ambiguity; when complex scenarios or creative needs are involved, key context must be retained to ensure information completeness. **Differences in task types:** - Instructional tasks (e.g., data extraction, format conversion): Conciseness is paramount, using clear verbs + core parameters and avoiding descriptive modifiers. - Creative tasks (e.g., copywriting, story generation): Need to include complete information such as style, audience, and core elements to prevent output deviation from expectations due to missing information. - Analytical tasks (e.g., problem diagnosis, trend prediction): Need to provide key details such as background data and constraints to ensure the completeness of analytical logic. It is recommended to first clarify the "core objective" and "boundary of necessary information," use the "necessity filtering method" to eliminate redundant descriptions, while retaining key constraints that affect output quality (e.g., format requirements, style preferences). The balance between conciseness and completeness can be dynamically adjusted by testing different versions of prompts and observing AI understanding biases.