Domain Knowledge in the AI Age: Where Things Fit
🇬🇧 English Version
Domain Knowledge in the AI Age: Where Things Fit
I’ve been thinking about something for months.
There’s a growing chorus—especially on social media—that “taste is everything” in the AI age. Beautiful interfaces. Elegant interactions. Aesthetic sensibility. These matter, clearly.
But I kept wondering: is that the whole picture?
I’m an engineer and PM by background. I’ve spent a decade building commercial products. And when I look at what actually makes products succeed, I see something else that’s equally fundamental—maybe more so.
What I’m Actually Seeing
It’s not “Do I have taste?”
It’s “Do I understand where things fit?”
Domain knowledge isn’t just knowing facts about an industry. It’s understanding the structure of problems and solutions within that space. It’s knowing:
- Where a new feature fits in the user journey
- Where a technical solution fits in the architecture
- Where a product positioning fits in the market landscape
This isn’t about dismissing aesthetic judgment. Good design matters. But even with AI assistance—maybe especially with AI assistance—you still need to understand where to place things before you worry about how they look.
Why This Matters More Now
AI can generate options. Many options.
- 100 design variations
- 50 feature ideas
- 20 architectural approaches
This is powerful. But it shifts the bottleneck.
The constraint is no longer “Can we create enough options?” It’s “Which option actually fits our problem?”
That judgment requires understanding the structure of your domain:
- Your users’ actual pain points (not generic user personas)
- Your technical constraints (not theoretical best practices)
- Your business model’s real leverage points (not what worked elsewhere)
Aesthetic taste helps you choose the most elegant option. Domain knowledge helps you know which options are even viable.
What I’ve Learned
Over the past decade building commercial products, I’ve noticed:
- Features that “looked right” but didn’t fit the user workflow → failed
- Technical solutions that were “elegant” but ignored business constraints → got rewritten
- Product positioning that was “compelling” but misread the market → didn’t resonate
The pattern: integration knowledge matters more than individual brilliance.
You need to know:
- Where features create value vs. complexity
- Where technical decisions have business consequences
- Where user needs intersect with technical feasibility
This isn’t “taste.” This is understanding system structure. And in the AI age, when anyone can generate polished options, understanding where things fit becomes the differentiator.
The Shift I’m Seeing
The most interesting products I’ve seen lately aren’t necessarily the most beautiful. They’re the ones where someone clearly understood the problem structure deeply enough to place each element exactly where it needed to be.
AI helps accelerate this. But it doesn’t replace the fundamental need to understand: where does this belong in the system?
That’s domain knowledge. And it’s not something you can prompt-engineer your way to—it comes from actually working in the domain, making mistakes, seeing what fails and why.
Writing this to clarify my own thinking as much as to share it.
🇨🇳 中文版本
AI 时代的领域认知：知道东西该放在哪里
有个问题我想了好几个月。
社交媒体上越来越多人在说”品味就是一切” —— 在 AI 时代。漂亮的界面、优雅的交互、美学感知力。这些当然重要。
但我一直在想：这是全部吗？
我的背景是工程师和产品经理。做了十年商业产品。当我看那些真正成功的产品时，我发现还有另一样东西同样关键 —— 也许更关键。
我观察到的
不是”我有没有品味？”
而是”我懂不懂东西该放在哪里？”
领域认知不只是知道一个行业的事实。而是理解这个领域里问题和解决方案的结构。是知道：
- 一个新功能在用户旅程中的位置
- 一个技术方案在架构中的位置
- 一个产品定位在市场格局中的位置
这不是说美学判断不重要。好设计当然重要。但即使有 AI 辅助——也许正是因为有 AI 辅助——你仍然需要先理解东西该放在哪里，再去考虑它该长什么样。
为什么现在更重要
AI 能生成选项。很多选项。
- 100 种设计变体
- 50 个功能想法
- 20 种架构方案
这很强大。但它改变了瓶颈所在。
约束不再是”我们能创造足够多的选项吗？”而是”哪个选项真正适合我们的问题？”
这个判断需要理解你所在领域的结构：
- 你的用户真正的痛点（不是泛泛的用户画像）
- 你的技术约束（不是理论最佳实践）
- 你的商业模式真正的杠杆点（不是别人的成功经验）
美学品味帮你选择最优雅的选项。领域认知帮你知道哪些选项是可行的。
我学到的
过去十年做商业产品，我注意到：
- “看起来对”但不符合用户工作流的功能 → 失败
- “优雅”但忽略商业约束的技术方案 → 被重写
- “有说服力”但误读市场的产品定位 → 没有共鸣
模式：整合认知比单点卓越更重要。
你需要知道：
- 哪些功能创造价值，哪些增加复杂度
- 技术决策在哪里产生商业后果
- 用户需求和技术可行性在哪里交汇
这不是”品味”。这是理解系统结构。在 AI 时代，当任何人都能生成精美选项时，理解东西该放在哪里成为了差异化优势。
我看到的转变
最近我看到的最有意思的产品，不一定是最漂亮的。而是那些创造者明显深刻理解了问题结构，把每个元素都放在了恰当位置的产品。
AI 帮助加速这个过程。但它无法取代最根本的需求：这个东西该放在系统的哪里？
这就是领域认知。这不是你能通过 prompt engineering 获得的 —— 它来自于真正在这个领域工作、犯错、看到什么失败以及为什么。
写这个是为了澄清自己的思考，也为了分享。