## Start with the Failures: How Most People Crash and Bur...

Start with the Failures: How Most People Crash and Burn the First Time They Use AI to Write Copy

As an indie developer, I used to firmly believe that "AI copywriting" meant entering a product name, clicking generate, then copying and pasting the output. After actually trying it, I found the failure patterns are remarkably consistent:

Trap 1: Kicking things off with just "help me write some marketing copy." The AI doesn't know who your product is for, what your competitors look like, or what users care about—so what comes out is a hybrid of a user manual and chicken soup for the soul: "Make your efficiency soar" and "Empower your every day." It reads smoothly, but not a single line will make users pull out their wallets.

Trap 2: Pasting your website's feature list in as-is. The AI dutifully builds everything around features, producing nothing but "supports XX, built-in XX, one-click XX." Features aren't selling points. Users never buy specs—they buy "what hassle can this thing solve for me."

Trap 3: One version for every channel. Posting the same copy to your WeChat official account, Xiaohongshu, app stores, and WeChat Moments—not only does the formatting break, but the tone is completely off. Xiaohongshu users swipe right past anything starting with "Dear users."

The common thread in these three traps: treating AI like a vending machine instead of a junior copywriter who needs a brief. What AI can do is amplify good input into lots of good output—it can't fill in the missing input for you.

The Right Approach: Break "One-Click Generation" into Three Steps

Step 1: Feed It First—Have AI Dig Out Selling Points from Your Features

Don't rush to have AI write copy. First, have it do translation: translating technical language into user benefits. Feed it your product description, target users, and competitive differences, and have the AI output a "selling point list" first. Then you manually filter and rank, confirming which points are real differentiators and which are just self-congratulation.

Step 2: Set the Structure—Generate Copy for One Channel at a Time

Generate separately based on channel characteristics. Xiaohongshu needs conversational, emotional language; app stores need scenario keywords front and center; landing pages need structure. Have AI tailor its output to each channel's "reader mindset + platform rules + length constraints," instead of one generic draft pasted everywhere.

Step 3: Create Variants—Batch-Generate A/B Test Material

Clickbait isn't mysticism—it's probability. Have AI generate 5-10 titles and openings from different angles based on the same selling point, run them in small-scale ad tests, and let the data speak. This is where AI truly shines: writing ten variants takes a human an afternoon; AI does it in seconds.

The Prompt Template I Use Now

你是一位有5年经验的增长营销文案，擅长为独立开发产品写投放素材。

【产品信息】
产品名称：{产品名}
一句话描述：{做什么的}
核心功能：{列3-5个关键功能}

【目标用户】
用户画像：{例如：刚上线的独立开发者，1-3人小团队}
他们当前的痛点：{例如：没有专职文案，写投放素材耗时}
他们尝试过但失败的方法：{可选}

【竞品差异】
我们与竞品最大的不同：{1-2条}

【任务】
1. 先把功能翻译成用户利益，输出一张「功能 → 卖点」对照表，标注哪个卖点最值得主推，说明理由
2. 基于主推卖点，分别生成：
   - 小红书笔记（口语化、有场景感、300字内）
   - 应用商店描述（突出关键词、结构化）
   - 朋友圈/社群短文案（50字内、有钩子）
3. 为主推卖点生成8个不同角度的标题，覆盖：痛点型、数据型、反常识型、提问型

【约束】
- 不使用"赋能""闭环""抓手"等空泛词汇
- 每条文案必须包含一个具体场景或具体动作
- 语气：{例如：专业但不端着}

The key to this template is asking for the comparison table first, then the copy—forcing the AI (and yourself) to think through the selling points before putting pen to paper.

Before and After: Same Afternoon, Completely Different Output

Before using AI (one-shot generation with a single prompt):

2-3 versions of copy with no room for revision, because I couldn't tell what was wrong
No A/B material before launch; picked titles entirely by gut feeling
One version reused across all channels, conversion left to chance
My mindset: AI writes fast but shallow—I didn't dare use it

After using AI (three-step process):

Got the selling-point comparison table first, confirmed the direction before generating—draft rejection rate dropped dramatically
20+ title variants per run, enough for two rounds of A/B testing
Independent versions for each channel, with tone and format matched to each platform
My time shifted from "writing copy" to "filtering and setting direction"—which is precisely what humans should be doing

By rough estimate, preparing one round of ad material used to take a day or two of grinding. Now the core work is maintaining that brief template—when the product updates, I just change a few fields and regenerate a whole new round.

A Few Reminders

AI copy is a draft, not a final version. Data, promises, and compliance statements must be reviewed by a human—especially wording involving performance guarantees.
You must rank the selling points yourself. AI can list them, but only you know which one is your real moat.
Feed data back regularly. Feed the winning copy from A/B tests back to the AI to analyze commonalities, and the next generation's quality will keep improving.
If you're batch-generating material via API, for cost control I recommend the efficiency funnel approach (I've written a dedicated post on this before); for tool pricing, refer to the official pricing page.

The tool isn't the mystery—the process is the moat. Upgrade "one-click generation" to "three-step generation," and you'll find that AI isn't writing copy for you; it's turning copywriting from an art into an assembly line—and the quality inspector on that line is always you.

If you want to try building your own AI copywriting workflow, start by registering an API account and running the template above: https://api.thistoken.ai/register

---

Tired of juggling provider integrations? Register at https://api.thistoken.ai/register and call every model through one base_url.