## First, Let's Look at the Failure Scenes

First, Let's Look at the Failure Scenes

Over the past six months, I've seen far too many indie developers and small teams use AI to write marketing copy like this:

Failure #1: One-shot generation from a single sentence. Open the chat box, type "help me write a promo copy for my expense-tracking app," and AI instantly returns a bland, middle-of-the-road text — "Smart expense tracking makes financial management easier." The developer frowns: this statement could apply to anyone, which means it says nothing. Then they conclude: "AI can't write copy."

Failure #2: Treating AI as a formatting tool. You struggle for half an hour writing a paragraph, then toss it to AI saying "polish this for me." It does read smoother afterward, but the core message is still the same few sentences — the selling points weren't excavated, the audience wasn't calibrated. You've essentially used two rounds of work to accomplish what you originally did in one.

Failure #3: Generate once, launch immediately. Generate three versions of copy, feel they're "close enough," and throw them straight into the ad platform. Three days later the numbers are flat, and you have no idea whether the problem is the copy, the creative assets, or the audience — because there's no control group, no version differentiation. The budget burns out and you're left completely confused.

The common thread in these three scenarios: users treated AI as a typist, not a collaborator in marketing strategy. AI's capabilities went untapped, yet it took the blame.

Where the Pain Points Really Are

When indie developers and small teams do promotion, what they lack was never writing ability. What they lack are three things:

1. No time for selling-point distillation. You've spent six months building the product and know every feature by heart, but you've never systematically sorted out "which features are must-haves for which types of users."
2. No energy for multiple versions. A single channel needs at minimum body copy, headlines, image captions, and a landing page hero — and multi-platform requires rewrites on top. Manually writing three versions is exhausting, yet advertising is precisely what requires testing.
3. No feedback loop. You write it, you launch it, and whether it worked is pure gut feeling. The next round starts from zero all over again.

AI happens to fill all three gaps — provided you give it structured input, not one vague instruction.

The Right Path: A Four-Step Process

Step 1: Feed It — Structure Your Product Information

Don't make AI guess your product. Prepare a fixed block of input: what the product is, who the target users are, what problem it solves, what differentiates it, and roughly what the competitors look like. Write this material once and reuse it endlessly.

Step 2: Excavate — Let AI Rank the Selling Points First

This step is the most easily skipped, yet the most critical. Have AI output 5–8 candidate selling points based on your input, annotating each with the audience and scenario it fits. Then you pick and refine — the decision-making power stays with you; AI handles the divergent thinking.

Step 3: Generate — Batch Production by Channel

Once selling points are selected, have AI produce the full set of launch-ready materials in one go: 3 versions of body copy, 5 headlines, image captions, and a landing page hero. Same selling point, different styles (rational comparison, pain-point scenario, emotional resonance) — naturally forming an A/B test version group.

Step 4: Feed Back — Pipe the Ad Data Back In

Whichever version gets the best clicks, tell AI directly: "Version 2 performed best — analyze its characteristics and produce five more versions in that direction." Copy production transforms from one-and-done into an iterative loop.

A Reusable Prompt Template

你是一位擅长效果广告的营销文案专家。请基于以下信息工作：

【产品信息】
产品名称：{填入}
一句话描述：{填入}
目标用户：{填入，越具体越好，如"刚独立开发第一款产品的程序员"}
核心痛点：{用户当前遇到的具体麻烦}
差异化优势：{与主流方案的差别}

【任务】
1. 先提炼5个候选卖点，每个卖点注明：适合的子人群、
   使用场景、能调动的情绪，并按"转化潜力"排序；
2. 等我确认卖点后，针对我选定的卖点生成：
   - 3版投放主文案（每版不超过60字，风格分别为：
     痛点共鸣型 / 数据理性型 / 场景故事型）
   - 5版短标题（每版不超过15字）
   - 1段配图文案（不超过30字）
   - 落地页首屏文案（标题+副标题+一句行动号召）
3. 所有文案禁止使用"赋能""极致""颠覆"等空洞词汇，
   必须包含具体场景或具体结果。

现在请先输出第1步的卖点分析。

Note the last line — have AI do the analysis first, then the generation, across two rounds of conversation. You can course-correct in the first round's output, which is far more efficient than getting a finished product and reworking it.

Before and After Using AI

The core change isn't "writing faster" — it's that the role changed: you go from writer to editor and decision-maker. AI handles divergence and production; you handle judgment and quality control. That's the division of labor each side is actually good at.

Three Pitfall Warnings

1. Don't skip the selling-point analysis and jump straight to copy. Skipping it leads right back to the failure scenes at the beginning.
2. The banned-words list is valuable. AI's default output carries a "marketing-speak" flavor; explicitly banning empty buzzwords noticeably increases the usability rate of the output.
3. Fact-check all generated copy by hand. Numbers, feature descriptions, promises — AI may overstate them, and once launched, that's a compliance risk.

Final Words

On the tooling level, this process doesn't demand much from the model. For everyday copy generation, lightweight models cost very little; switch to a stronger model only when you need deep selling-point analysis — pairing fast and slow models keeps the economics sensible. If you don't yet have a convenient model access channel, check out this platform — you can register and start running the process above right away: https://api.thistoken.ai/register

First, kick the failed habit of "one-shot generation from a single sentence" — the road ahead will get much smoother.

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