How to diagnose and optimize ambiguous or vague expressions in prompts?

When there are ambiguities or vague expressions in the prompt, system diagnosis and targeted optimization can be used to improve the clarity of the instructions. Diagnosis should focus on three core issues: multiple meanings of vocabulary, unclear references, and lack of context; optimization should start from three aspects: precision of expression, background supplementation, and quantitative description. Diagnosis methods: - Lexical ambiguity: Check for polysemous words (e.g., "processing data" can mean analyzing or deleting); - Unclear reference: Identify vague pronouns (e.g., "this plan" does not specify the exact object); - Lack of context: Determine if there is a lack of necessary premises (e.g., not specifying "target users" leads to vague requirements). Optimization strategies: - Replace vague vocabulary: Use specific expressions instead of abstract descriptions (e.g., change "complete as soon as possible" to "submit the first draft within 3 days"); - Supplement background information: Clarify the task scenario (e.g., "design summer promotion copy for an e-commerce platform" instead of just "write promotion copy"); - Quantify demand indicators: Add measurable standards (e.g., "generate 5 titles containing the keyword 'environmental protection'"). It is recommended to conduct "prompt-output" comparison tests, adjust expressions gradually, prioritize solving recurring understanding deviations, and continuously improve the accuracy of instruction delivery.