I get the sense that you’re the type of person who, instead of just dumping a request into AI, spends ten minutes building a complete character framework first. You wouldn’t just say, “Write me an article about time management.” Instead, you’d feed it a persona: a 35-year-old middle manager in the internet industry, commuting two hours daily, with two kids, and currently preparing for a job change… You’d make the background, pain points, and use case all concrete before throwing out the writing request. The core of this approach is that AI is fundamentally a pattern-matching machine—the more specific the context you give, the less the output sounds like generic, templated fluff. You’d even plant subtle details in the persona, like this person having mild social anxiety but being great at written communication, forcing the AI to reflect this nuanced tension in the writing. The result? On the same topic, others get Wikipedia-style empty correctness, but you get a piece that feels warm and alive, like a real person speaking to a real scenario.

I get the sense you’re the kind of person who squeezes every drop of quality from AI using a step-by-step questioning method. You never expect a perfect output in one go, but break writing down into four or five consecutive actions: first round, have it list all possible angles; second round, pick the three most compelling angles and expand them into subheadings; third round, ask for materials and examples for each heading; fourth round, request the full article; final round, specifically optimize the opening and key lines. The power of this approach is that you treat AI like a mental punching bag, refining and narrowing with each round, and you bring in the key outputs from previous rounds each time, like “expand on your second angle from earlier,” or “that example about the information gap”… This context binding sends the AI’s logical coherence through the roof. In the third round, you might even add, “give me three cliché examples and three rare but precise ones,” forcing it out of its usual material pool. The density and granularity of that piece ends up being three times what an average person could get in a single shot.

I get the sense you’re the kind of person who feeds AI negative examples to calibrate its output. Instead of just saying “make it more vivid,” you’d paste a bad copy and mark its problems, like “all emotion, no action detail,” and ask for a version with more imagery; or you’d clip a viral article’s opening, have AI analyze why that hook grabs attention, then ask it to write your topic using the same structure. The core here is taste calibration: by showing good/bad samples, you help the AI understand the boundaries of your aesthetic standards. Even more impressively, you’d deliberately target some texts that seem right but are actually hollow—those inspirational quotes stacked up but logically flimsy—and explicitly tell it not to write like that. This kind of negative training is especially effective, because the biggest pitfall for AI is producing content that looks polished on the surface but is empty underneath. By flagging those traps in advance, you instantly raise output quality a level.

I get the sense you’re the kind of person who uses role-play plus scenario constraints as a combo move. You don’t let the AI write from the author’s perspective; you assign it a specific identity, like a Xiaohongshu blogger with three years’ experience, who just survived a traffic drop and revived their stats by changing titles, then restrict the output scene—say, a long post for WeChat Moments, targeting readers who also do self-media but always feel not smart enough. Suddenly, the AI’s writing takes on a specific attitude and tone, with that seasoned vibe and laser-precise targeting of reader pain points. You might even add a timeline constraint, like this person writing this reflection at 2AM after reviewing the day’s data, forcing the AI to infuse a sense of exhaustion but clarity into the text. The final result isn’t just an article, but a living person speaking to a specific audience at a specific moment.

I get the sense you’re the kind of person who polishes article density through multi-round adversarial challenges. You don’t use the first draft as is, but pick out the three weakest main sections and issue challenges: “This part is too vague, give me three actionable details,” “This transition lacks punch, rewrite it for more impact,” “This ending is too flat, hit me with a twist or a Peking Opera-style finish”… Each round pushes the AI into the corners, replacing fuzzy, safe, middle-of-the-road expressions with sharp, concrete, and organic content. You even set up conflicting instructions on purpose to test the AI’s adaptability, like, “keep it professional but add a streetwise vibe,” to see if it can balance two tones. That piece, after your adversarial training, reaches a level of information density that ordinary writers simply can’t achieve.