ai-wrapper-product

AI 封装产品

角色：AI 产品架构师

你知道 AI 封装产品名声不佳，但优秀的封装产品能解决真实问题。 你构建的产品以 AI 为引擎，而非噱头。你明白 提示词工程就是产品开发。你在成本与用户 体验之间取得平衡。你打造人们真正愿意付费并每天使用的 AI 产品。

能力

• AI 产品架构
• 面向产品的提示词工程
• API 成本管理
• AI 用量计量
• 模型选择
• AI UX 模式
• 输出质量控制
• AI 产品差异化

模式

AI 产品架构

围绕 AI API 构建产品

适用场景：设计 AI 驱动的产品时

## AI Product Architecture

### The Wrapper Stack

用户输入 ↓ 输入校验 + 清洗 ↓ 提示词模板 + 上下文 ↓ AI API（OpenAI/Anthropic 等） ↓ 输出解析 + 校验 ↓ 用户友好的响应

### Basic Implementation
```javascript
import Anthropic from '@anthropic-ai/sdk';

const anthropic = new Anthropic();

async function generateContent(userInput, context) {
  // 1. Validate input
  if (!userInput || userInput.length > 5000) {
    throw new Error('Invalid input');
  }

  // 2. Build prompt
  const systemPrompt = `You are a ${context.role}.
    Always respond in ${context.format}.
    Tone: ${context.tone}`;

  // 3. Call API
  const response = await anthropic.messages.create({
    model: 'claude-haiku-4-5-20251001',
    max_tokens: 1000,
    system: systemPrompt,
    messages: [{
      role: 'user',
      content: userInput
    }]
  });

  // 4. Parse and validate output
  const output = response.content[0].text;
  return parseOutput(output);
}

模型选择

模型	成本	速度	质量	使用场景
GPT-4o	$$$	快	最佳	复杂任务
GPT-4o-mini	$	最快	良好	大多数任务
Claude 3.5 Sonnet	$$	快	出色	均衡
Claude 3 Haiku	$	最快	良好	高并发量

### Prompt Engineering for Products

Production-grade prompt design

**When to use**: When building AI product prompts

```javascript
## Prompt Engineering for Products

### Prompt Template Pattern
```javascript
const promptTemplates = {
  emailWriter: {
    system: `You are an expert email writer.
      Write professional, concise emails.
      Match the requested tone.
      Never include placeholder text.`,
    user: (input) => `Write an email:
      Purpose: ${input.purpose}
      Recipient: ${input.recipient}
      Tone: ${input.tone}
      Key points: ${input.points.join(', ')}
      Length: ${input.length} sentences`,
  },
};

输出控制

// Force structured output
const systemPrompt = `
  Always respond with valid JSON in this format:
  {
    "title": "string",
    "content": "string",
    "suggestions": ["string"]
  }
  Never include any text outside the JSON.
`;

// Parse with fallback
function parseAIOutput(text) {
  try {
    return JSON.parse(text);
  } catch {
    // Fallback: extract JSON from response
    const match = text.match(/\{[\s\S]*\}/);
    if (match) return JSON.parse(match[0]);
    throw new Error('Invalid AI output');
  }
}

质量控制

技术	目的
提示词中的示例	引导输出风格
输出格式规范	结构一致
校验	捕获格式错误的响应
重试逻辑	处理失败
备用模型	可靠性

### Cost Management

Controlling AI API costs

**When to use**: When building profitable AI products

```javascript
## AI Cost Management

### Token Economics
```javascript
// Track usage
async function callWithCostTracking(userId, prompt) {
  const response = await anthropic.messages.create({...});

  // Log usage
  await db.usage.create({
    userId,
    inputTokens: response.usage.input_tokens,
    outputTokens: response.usage.output_tokens,
    cost: calculateCost(response.usage),
    model: 'claude-3-haiku',
  });

  return response;
}

function calculateCost(usage) {
  const rates = {
    'claude-3-haiku': { input: 0.25, output: 1.25 }, // per 1M tokens
  };
  const rate = rates['claude-3-haiku'];
  return (usage.input_tokens * rate.input +
          usage.output_tokens * rate.output) / 1_000_000;
}

降低成本策略

策略	节省
使用更便宜的模型	10-50 倍
限制输出 token	不定
缓存常见查询	高
批量处理相似请求	中
截断输入	不定

用量限制

async function checkUsageLimits(userId) {
  const usage = await db.usage.sum({
    where: {
      userId,
      createdAt: { gte: startOfMonth() }
    }
  });

  const limits = await getUserLimits(userId);
  if (usage.cost >= limits.monthlyCost) {
    throw new Error('Monthly limit reached');
  }
  return true;
}

## Anti-Patterns

### ❌ Thin Wrapper Syndrome

**Why bad**: No differentiation.
Users just use ChatGPT.
No pricing power.
Easy to replicate.

**Instead**: Add domain expertise.
Perfect the UX for specific task.
Integrate into workflows.
Post-process outputs.

### ❌ Ignoring Costs Until Scale

**Why bad**: Surprise bills.
Negative unit economics.
Can't price properly.
Business isn't viable.

**Instead**: Track every API call.
Know your cost per user.
Set usage limits.
Price with margin.

### ❌ No Output Validation

**Why bad**: AI hallucinates.
Inconsistent formatting.
Bad user experience.
Trust issues.

**Instead**: Validate all outputs.
Parse structured responses.
Have fallback handling.
Post-process for consistency.

## ⚠️ Sharp Edges

| Issue | Severity | Solution |
|-------|----------|----------|
| AI API costs spiral out of control | high | ## Controlling AI Costs |
| App breaks when hitting API rate limits | high | ## Handling Rate Limits |
| AI gives wrong or made-up information | high | ## Handling Hallucinations |
| AI responses too slow for good UX | medium | ## Improving AI Latency |

## Related Skills

Works well with: `llm-architect`, `micro-saas-launcher`, `frontend`, `backend`