As an indie developer or a small team member, you've surely experienced scenarios like this:

• User feedback is scattered across WeChat groups, emails, App Store reviews, surveys, and support tickets, in all kinds of formats;
• Hundreds of raw feedback entries pile up in spreadsheets, and just reading through them takes a whole day;
• After finally organizing everything, someone at the product review meeting asks, "How do you know this requirement is the most urgent?"
• Next week another batch of feedback arrives, and your previous organization work has to be almost completely redone.

The problem with the traditional approach: high organization costs, inconsistent standards, no quantification, and poor reusability. Manual categorization is prone to emotional and memory bias—often you end up remembering only the loudest users, while the needs of the silent majority get drowned out.

AI happens to excel at exactly this kind of work: high volume, tedious, and requiring consistency.

II. What AI Can Do for Users

In the requirement-organizing scenario, AI can take on four tasks:

1. Batch cleaning and structuring: Convert raw feedback that's colloquial, emotional, and full of typos into structured entries (feature module, problem description, expected solution, user sentiment).

2. Automatic clustering and deduplication: 500 pieces of feedback phrased differently might essentially be just 30 requirement points. AI can recognize that "loads too slowly," "takes forever to open," and "laggy" are the same issue, and count mention frequency.

3. Multi-dimensional scoring and ranking: AI can score each requirement according to a framework you define (e.g., impact scope, urgency, implementation cost, business value) and produce an explainable priority ranking instead of gut-feel decisions.

4. Generating deliverable documents: Directly output requirement list tables, priority matrix explanations, and even reply scripts for users.

Your role shifts from "organizer" to "reviewer"—you only make the final judgments, not the repetitive labor.

III. AI Workflow (Five Steps)

Step 1: Collect and aggregate. Export feedback from all channels into a unified format (CSV or plain text recommended, one entry per line, including channel, date, and original text).

Step 2: Define the categorization framework. First, have AI read a small sample batch (around 50 entries) and generate a suggested taxonomy, then fine-tune it manually. This step is critical—once the framework is set, all subsequent batches can stay consistent.

Step 3: Process in batches. Feed the AI 100–200 entries per batch using a unified prompt template (see below), requesting structured output as JSON or tables. Batching both avoids quality degradation from overly long context and makes spot-checking easier.

Step 4: Aggregate and score. Merge results from all batches, have AI deduplicate and cluster, count frequencies, and score according to your priority formula.

Step 5: Human review of output. Focus on whether the Top 10 requirements match your intuition, spot-check 5% of the categorization results, make corrections, and it's ready for sprint planning review.

IV. A Reusable Prompt Template

你是一位资深产品经理，擅长需求分析。请对我提供的用户反馈进行整理和优先级评估。

【背景信息】
产品类型：[填写：如工具类App/SaaS软件]
目标用户：[填写：如中小企业运营人员]
产品阶段：[填写：如早期MVP/成长期]

【任务要求】
1. 将每条反馈结构化为：功能模块 | 问题/诉求描述 | 用户期望
2. 将语义相同的反馈合并为一个需求点，并统计提及次数
3. 按以下维度为每个需求点打分（1-5分）：
   - 影响面（多少用户提及或受影响）
   - 紧急度（是否阻塞核心流程、是否流失信号）
   - 实现成本（1分=很低，5分=很高，注意反向指标）
   - 商业价值（对留存/付费的潜在贡献）
4. 计算综合优先级得分 = 影响面×0.35 + 紧急度×0.3 + 商业价值×0.2 + (6-实现成本)×0.15
5. 输出格式：Markdown表格，包含需求点、提及次数、各维度得分、综合得分、优先级（P0/P1/P2/P3）、一句话建议
6. 最后用3-5句话总结需求全景，指出最值得立即处理的1-2件事

【注意事项】
- 用户情绪激烈但诉求模糊的反馈，标记为"待澄清"而不是猜测
- 不要发明反馈中不存在的需求
- 权重可调整，我会根据团队情况修改

【用户反馈数据】
[粘贴反馈内容，每条一行]

The core design of this template: explicit scoring dimensions + adjustable weights + forced output format. The former makes rankings explainable and reproducible; the latter lets you paste results directly into documents or spreadsheets.

V. Before-and-After Comparison

It must be emphasized that AI doesn't make decisions for you—AI may estimate "implementation cost" inaccurately (it doesn't know your codebase), so you must calibrate it manually; requirements involving strategic trade-offs also require your business judgment. AI's value is compressing 80% of the repetitive labor so you can apply your 20% of judgment where it matters most.

VI. Practical Tips

1. Start small, then scale: Run the process with 100 feedback entries first, validate the framework makes sense, then process in bulk.
2. Keep the raw data: AI's categorization results should be traceable back to the original feedback for easy verification.
3. Iterate the framework regularly: When your product stage changes (e.g., from MVP to growth phase), the priority weights should be adjusted too.
4. Beware of survivorship bias: Users who proactively give feedback are inherently biased; cross-validate with analytics event data.

Final Thoughts

Delegating high-effort, low-creativity work like feedback organization to AI is one of the most immediately effective ways for indie developers and small teams to put AI into practice—low investment, clear workflow, and visible output the same day.

If you want to further automate this process (e.g., scheduled feedback pulls, automatic AI processing, pushing results to Feishu or Notion), you can do so via APIs. Here's a convenient channel for getting AI API access—register to get a stable model API and quickly build your own feedback processing pipeline:

👉 https://api.thistoken.ai/register

Let AI handle the noise, so you can focus on the decisions that truly matter for your product.

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Want to run the example right away? Visit https://api.thistoken.ai/register to sign up for ThisToken.AI and get started once you have your API Key.