Last week, I helped a friend who's starting a business revise their fundraising proposal. I used three meticulous attention to detail methods, and the final proposal was praised by investors for "even considering whether the shipping fee included tax." This reminded me of:AI's answer isn't "just give it a signal and it's right," but rather you need to "actively scrutinize the details" to truly apply its capabilities.。
Here are three methods I've personally tested and found effective, along with specific prompt examples, to help you extract the most reliable data from AI:
1. Let AI "write in steps" to avoid "missing steps".“
When writing a proposal, do you often feel that "the AI's answer is missing a link"? For example, when writing a "pet supplies online promotion plan", the AI may directly suggest "make short video ads", but it doesn't explain "why choose short videos", "who the target users are", or "how to allocate the budget" - this is "the problem is not broken down, and the AI gives a random answer".
Solution: Let AI answer step by step.“Break down the problem like "disassembling building blocks" into "1. Current situation analysis 2. Goal breakdown 3. Implementation steps".
- Original question"Write an online promotion plan for pet supplies" (AI may only provide one direction)
- Prompt"Break it down in three steps: 1. Analyze the online behavior habits of 'pet owners' (mainly women aged 25-35) (time spent browsing short videos/Xiaohongshu); 2. Design 3 promotion goals (gain 1000 followers / conversion rate of 5% / repurchase rate of 30%); 3. List 3 specific actions (Xiaohongshu reviews / Douyin challenges / private domain community operations), and write down the time, budget, and expected results for each action."“
- EffectAI will first analyze "pet-owning women's most concerned 'pet health' and 'cost-effectiveness' when browsing Xiaohongshu", and then provide "3 review notes per week + 'show off your pet to win a free order' challenge", with the budget precisely set at "notes 1,000 yuan / challenge 2,000 yuan" - the plan will no longer be "like a castle in the air".
II. Multi-model "complementary questioning" allows AI to "show its unique strengths".“
Different AIs have vastly different areas of expertise: GPT is good at accurate data processing, Claude excels at writing emotional copy, and Gemini understands viral memes. Asking only one AI is like "trying to hammer all nails with one hammer"—some nails won't go in, and some will be hammered crookedly.
Solution: Ask questions based on the strengths of different models, allowing them to "divide the work" and cooperate.“Then use DiffMind Integration.
- SceneHelping a friend write a campus event plan for "College Students' Anti-Involution"
- Questions based on division of labor：
- For GPT: "Calculate event costs (venue rental/promotional materials/prizes), create a budget (accurate to the yuan), and mark 'which expenses can be reduced'";
- For Claude: "Write the copy for the event poster, highlighting the emotional resonance of 'rejecting involution' (such as 'Don't let early morning memorization become 'ineffective effort'), and use 3 real stories of college students to enhance the sense of immersion."
- For Gemini: "Design a campaign to spread the message (such as '# College Student Anti-Cooling Code'), and plan two online interactions (@students share their anti-cooling declarations/win anti-cooling manuals in the comments section)";
- IntegrationUsing DiffMind to view the answers from three AIs simultaneously, I copied GPT's budget sheet, Claude's copywriting, and Gemini's topics, instantly creating an event planning document with "full detail."
Third, use "inverse questions" to verify AI: "If I'm wrong, what will you say?"“
AI's answers often contain logical flaws: for example, it might state "anti-involution requires rejecting all involutionary behaviors," but ignore the value of "moderate effort." In such cases, don't rush to use it; first ask it, "If I question this viewpoint, what flaws do you see?"“
Solution: Pose "counter-questions" to the AI and let it "find fault" itself.“This exposes underlying assumptions or logical flaws.
- Original answer"Anti-cohesion = Rejecting involution; all actions that involve 'working one hour more than others' constitute involution."“
- Inverse Prompt"You said 'all efforts are involution.' If I question 'moderate effort is for self-improvement and does not count as involution,' how should I respond? Please point out two assumptions underpinning this conclusion (e.g., 'more effort = surpassing others'), then add three core differences between 'moderate effort' and 'involution' (using data or examples), and finally give an action suggestion for 'balanced effort'."“
- EffectAI will acknowledge that "the premise is that 'more effort will inevitably lead to a comparative mentality'", and give examples such as "'memorizing words for 1 hour every day' is self-improvement, while 'memorizing for 2 hours more to surpass your deskmate' is involution" - the answer instantly changes from "absolute" to "with boundaries", avoiding the pitfall of "one-size-fits-all".
In summary, the reliable answer is "you and AI work together to 'find the details'."“
The core of these three methods is to transform you from "passively accepting AI answers" to "actively guiding AI thinking": breaking down the problem step by step to avoid "missing steps"; using multiple models to complement each other in asking questions, allowing AI to "show its unique strengths"; and using counter-questions for verification to expose "vulnerabilities". DiffMind is the "tool" that helps you achieve all of this—no need to switch between three AI platforms, one window displays step-by-step answers, compares multiple models, and verifies counter-questions, allowing you and AI to "examine the details together and find reliable answers together".
The next time you use AI, try these three methods—you'll find that what's more important than "making AI smarter" is "making yourself better at asking questions."
