First, Let Me Describe the Failures I've Seen

As someone who frequently works with indie developers and small teams, I've seen plenty of people try to use AI to write marketing copy, only to conclude: "What AI writes is unusable."

Once, a friend who built a productivity tool showed me his process: he copied the entire "About Us" page from his product's website into the chat box, typed "help me write a promotional copy," and the AI produced a piece of bland, middle-of-the-road text—full of empty phrases like "empowering efficient work" and "making productivity within reach." He posted it to an ad platform, the click-through rate was dismal, and from then on he was convinced AI copywriting was unreliable.

This isn't an isolated case. There are roughly three common failure patterns:

Pattern one: Garbage in, expecting gold out. The product materials are outdated manuals from a year ago, without even the core selling points distilled. The AI can only spin around in a pile of feature descriptions, and what it writes sounds like a parrot repeating the product documentation.

Pattern two: One-sentence instructions, expecting professional output. An instruction like "write me an ad slogan" is equivalent to asking an intern who just joined the company and knows nothing about the product to deliver final copy on the spot. Even a human intern would need a requirements meeting—how could AI possibly guess your audience and tone out of thin air?

Pattern three: Running AI's first draft directly as an ad. The first version AI generates is often "correct but boring." With no iteration, no feedback, no A/B variants, putting real budget behind a single version of copy isn't an AI problem—it's a process problem.

The common thread in these failures is: users treat AI like a vending machine—insert coin, receive product, done. In reality, AI can play a much richer role in this scenario.

What AI Can Actually Do in This Workflow

If you break down "generating marketing copy," it's actually a complete pipeline:

Distill product selling points → Match audience language → Generate multiple copy versions → Adapt to different ad channels → Output ready-to-use material packages.

Along this pipeline, things AI can do include:

1. Selling point distillation: Turn messy product descriptions (or even voice-to-text notes) into a structured list of selling points, flagging which are differentiated advantages and which are commodity features.
2. Audience translation: The same selling point needs completely different phrasing for tech geeks versus moms. AI can generate multiple language styles for different audiences.
3. Batch variant generation: Feed ads need large numbers of copy variants for testing. Writing 20 versions manually takes a day; AI produces first drafts in minutes, and humans do the filtering.
4. Channel adaptation: Xiaohongshu notes, app store descriptions, landing page heroes, push notifications—each has different formats, lengths, and tones. AI can output everything to channel specifications in one go.
5. Diagnosis and optimization: Feed underperforming copy back to AI, have it diagnose the problem (unclear selling point? Weak hook?) and suggest revision directions.

In other words, AI isn't writing copy for you—it turns "copy production" from a handcraft workshop into an assembly line. You handle quality control, it handles throughput.

The Right Workflow: Four Steps

Step 1: Feed it material—give AI enough context

Prepare three types of information: what the product is (features, positioning, price range), who it's for (target user personas, their pain points), and where it will run (channels, what competitors are saying). Even a rough description of a few sentences is ten times better than a single "help me write copy."

Step 2: Ask for structure first, copy second

Don't ask for finished products directly. First have AI output a list of selling points and a copy framework; once you confirm the direction is right, let it expand into full copy. If the direction is wrong, no matter how well it's written, it's wasted.

Step 3: Generate in batches, filter manually

Have AI output 5–10 versions at once, with stylistic differences (rational, scenario-based, comparison-based). You pick the 2–3 that feel right, and have it iterate on variants from there.

Step 4: Transform per channel

Have AI adapt the selected copy into formats for each channel, export the material list, and go straight into ad placement.

A Reusable Prompt Template

My team has used the following template for a long time—just replace the bracketed content:

你是一位有 [3] 年 [信息流广告] 经验的文案策划。

【产品信息】
产品名称：[产品名]
核心功能：[1-3个核心功能]
差异化优势：[相比竞品强在哪]
价格情况：[描述定价模式，具体数字以官网价格页为准]

【目标受众】
人群：[如：25-35岁一线城市职场人]
核心痛点：[他们现在遇到什么问题]
决策动机：[他们为什么愿意尝试新产品]

【任务】
1. 先提炼3-5条产品卖点，标注每条对应哪个受众痛点
2. 针对每条卖点，各写2个文案版本：一个理性风格（数据/功能导向），一个场景风格（讲故事/情绪导向）
3. 每个版本不超过[30]字，包含一个钩子和一个行动暗示

【输出格式】
| 卖点 | 对应痛点 | 理性版 | 场景版 |

先只输出这个表格，等我确认方向后再展开。

Note the last sentence—"first only output the table, wait for my confirmation." This sentence is the soul of the entire template: it forcibly splits the process into two steps, preventing AI from producing a pile of long copy in the wrong direction all at once.

Before and After Using AI

The biggest change isn't "writing faster," but daring to test. Before, you'd bet everything on one version of copy—couldn't revise it, couldn't afford to test it; now every selling point and every style can be validated at low cost, and ad decisions go from "gambling" to "filtering."

Final Thoughts

Back to those failures at the beginning—the problem was never AI's capability, but how it was used. Treat AI as a typist, and it will only parrot; treat AI as a core station on the assembly line, equip it with clear instructions and human quality checks, and it can multiply a small team's copy output several times over.

If you're also looking for a stable model API for this kind of copy generation (whether to integrate into your own product or build internal tools), you can start with an OpenAI-compatible multi-model gateway service—register here: https://api.thistoken.ai/register —running comparative tests across multiple models on copy style differences is itself a practical technique for improving output quality.

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