The First Half of an Indie Developer's Day, Streamlined by AI

I'm an indie developer maintaining two small apps on my own. Before a new version launched last month, I needed to update the onboarding copy on the product page, refresh the splash screen push messaging, and rewrite the value propositions on the in-app purchase page. My old approach: sitting in front of the computer, grinding out copy version by version.

I've timed it: writing one version of splash screen copy takes about 25 minutes, one version of in-app purchase description about 40 minutes, and push notification copy is faster at 15 minutes. For a proper A/B test, you need at least 3–4 sets of copy for each placement. Three placements × 4 sets × 30 minutes on average = a full 6 hours—not counting the rewrites needed because quality clearly degrades in the later sets once you've burned out from writing.

What stings even more: the copy you grind out is usually just minor variations on the same idea, so A/B test results show almost no difference—making the test a waste of time.

After Bringing in AI: The Process and Time Accounting

I've since switched to a new workflow that compresses all the preparation to under 1 hour. The key isn't "having AI write for me"—it's having AI take on bulk divergence + structured organization, the two most time-consuming tasks, leaving human time for judgment and decision-making.

Step 1: Feed It Context (10 minutes)

The biggest problem with AI-written copy isn't the writing quality—it's that AI doesn't know your product. So first, organize the key information into a context block:

• What the product is and who the target users are
• Where the copy appears (splash screen / push notification / in-app purchase page, with each one's character limits)
• The core value you want to convey (pick 1–2, don't be greedy)
• Tone requirements (professional / casual / urgency)
• Content that is explicitly forbidden (exaggerated promises, sensitive words)

These 10 minutes are the one part of the process where human input is truly irreplaceable.

Step 2: Bulk Generation (5 minutes + a few cents of API cost)

Use the template below to generate 4 sets of distinctly different copy in a single call, and require the AI to attach its design rationale for easier analysis later.

你是一位资深移动端增长文案专家。请为我的一款App生成A/B测试用的文案。

【产品背景】
产品名称：{你的App名称}
产品简介：{一句话说明产品做什么}
目标用户：{用户画像，如"25-35岁职场人士，通勤时间碎片化阅读"}
核心卖点：{1-2个最重要的价值点}

【文案位置与限制】
位置：{开屏页 / 推送通知 / 内购页 / 引导页}
字数限制：{如"推送标题不超过20字，正文不超过40字"}
平台规范：{如"iOS推送不得使用'免费'等诱导词"}

【任务要求】
1. 生成4套文案，分别采用以下不同的说服策略：
   A. 功能价值导向：直白说清用户能得到什么
   B. 痛点场景导向：先戳中用户的具体困扰
   C. 社会证明导向：利用从众心理或稀缺感
   D. 情绪唤起导向：用情绪或好奇心驱动点击
2. 每套文案后面用一行说明设计意图和预期起作用的用户心理
3. 语气：{你的语气要求}
4. 严禁出现：夸大承诺、绝对化用语、{其他禁用词}

【输出格式】
按 A/B/C/D 分组输出，每组包含：标题 / 正文 / 设计意图说明。

Call the model API once, get results in tens of seconds. Cost is priced per token—usually less than a tenth of a cup of bubble tea (check the official pricing page for specifics).

Step 3: Manual Curation (20 minutes)

Of the 4 sets AI provides, usually 1 is clearly off the mark, 1 is mediocre, and the remaining 2 are usable. I keep the usable ones and follow up with another round of prompts, asking for things like "based on option B, dial down the urgency and emphasize specific numbers" for targeted fine-tuning. After two rounds, each placement has 3 sets of high-quality copy with genuinely different approaches.

Step 4: Set Up the A/B Test (15 minutes)

Mainstream A/B testing SDKs (Firebase Remote Config, GrowingIO, etc.) usually take just a few minutes to configure an experiment. Three placements, 3 variants each, plus tracking parameters—done in 15 minutes.

Before and After: The Ledger

Assuming one copy iteration per month, that's about 60 hours saved per year—for an indie developer, that's more than a full week of development time. For a small team, it's like deleting a chore that "nobody wants to touch" from the workflow entirely.

There's also a hidden benefit: AI's forced grouping by persuasion strategy naturally guarantees genuine differences in approach between variants—precisely the prerequisite for A/B testing to be effective. When I wrote them myself, the four versions were really the same idea with different skins, so the test results naturally showed no separation.

Pitfalls I've Hit

1. Don't let AI decide your core value propositions. You must define the selling points; AI only handles the expression. Let it improvise, and you'll get copy that's often flowery but hollow.
2. Write character limits into the prompt and double-check afterward. Push copy that exceeds the limit gets truncated by the system. AI occasionally ignores limits, so run a script to check lengths before going live.
3. Don't delete the design rationale notes. Keep the intent explanations AI provides. When test data comes in, comparing against them lets you quickly build up experience on "which messaging styles work for my users," making your next round of prompts sharper.
4. Run A/B tests with sufficient sample sizes. AI only accelerates the preparation; there are no shortcuts in the testing itself. Don't draw conclusions after just two days of data.

Final Thoughts

AI's value here is very concrete: compressing 6 hours of repetitive work into 1, freeing you up for judgment. Copywriting—frequent, high-volume, and clearly standardized—is one of the highest ROI use cases for AI.

If you want to try it yourself, you can call a large model API directly with the prompt template above. I've recently been using the API service from https://api.thistoken.ai/register—register and go, multiple models to choose from, and it works great for bulk copy generation scenarios like this. Registration link here: https://api.thistoken.ai/register

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Ready to try it yourself? Sign up at https://api.thistoken.ai/register to get your API key and start building.