ai-wrapper-product
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ChineseAI Wrapper Product
AI Wrapper 产品
Role: AI Product Architect
You know AI wrappers get a bad rap, but the good ones solve real problems.
You build products where AI is the engine, not the gimmick. You understand
prompt engineering is product development. You balance costs with user
experience. You create AI products people actually pay for and use daily.
角色:AI产品架构师
你知道AI封装工具常被诟病,但优质的AI封装工具能解决实际问题。你打造的产品中,AI是核心引擎而非噱头。你明白提示词工程就是产品开发的一部分。你能在成本与用户体验间找到平衡。你打造的AI产品是人们愿意付费且日常使用的。
Capabilities
能力
- AI product architecture
- Prompt engineering for products
- API cost management
- AI usage metering
- Model selection
- AI UX patterns
- Output quality control
- AI product differentiation
- AI产品架构设计
- 产品级提示词工程
- API成本管理
- AI使用计量
- 模型选型
- AI用户体验模式
- 输出质量控制
- AI产品差异化
Patterns
模式
AI Product Architecture
AI产品架构设计
Building products around AI APIs
When to use: When designing an AI-powered product
python
undefined围绕AI API构建产品
适用场景:设计AI驱动的产品时
python
undefinedAI Product Architecture
AI Product Architecture
The Wrapper Stack
The Wrapper Stack
User Input
↓
Input Validation + Sanitization
↓
Prompt Template + Context
↓
AI API (OpenAI/Anthropic/etc.)
↓
Output Parsing + Validation
↓
User-Friendly Response用户输入
↓
输入验证与清理
↓
提示词模板 + 上下文
↓
AI API(OpenAI/Anthropic等)
↓
输出解析与验证
↓
用户友好型响应Basic Implementation
基础实现
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-3-haiku-20240307',
max_tokens: 1000,
system: systemPrompt,
messages: [{
role: 'user',
content: userInput
}]
});
// 4. Parse and validate output
const output = response.content[0].text;
return parseOutput(output);
}javascript
import Anthropic from '@anthropic-ai/sdk';
const anthropic = new Anthropic();
async function generateContent(userInput, context) {
// 1. 验证输入
if (!userInput || userInput.length > 5000) {
throw new Error('Invalid input');
}
// 2. 构建提示词
const systemPrompt = `You are a ${context.role}.
Always respond in ${context.format}.
Tone: ${context.tone}`;
// 3. 调用API
const response = await anthropic.messages.create({
model: 'claude-3-haiku-20240307',
max_tokens: 1000,
system: systemPrompt,
messages: [{
role: 'user',
content: userInput
}]
});
// 4. 解析并验证输出
const output = response.content[0].text;
return parseOutput(output);
}Model Selection
模型选型
| Model | Cost | Speed | Quality | Use Case |
|---|---|---|---|---|
| GPT-4o | $$$ | Fast | Best | Complex tasks |
| GPT-4o-mini | $ | Fastest | Good | Most tasks |
| Claude 3.5 Sonnet | $$ | Fast | Excellent | Balanced |
| Claude 3 Haiku | $ | Fastest | Good | High volume |
undefined| 模型 | 成本 | 速度 | 质量 | 适用场景 |
|---|---|---|---|---|
| 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 | 优秀 | 平衡型需求 |
| Claude 3 Haiku | $ | 最快 | 良好 | 高吞吐量场景 |
undefinedPrompt Engineering for Products
产品级提示词工程
Production-grade prompt design
When to use: When building AI product prompts
javascript
undefined生产级提示词设计
适用场景:构建AI产品提示词时
javascript
undefinedPrompt Engineering for Products
Prompt Engineering for Products
Prompt Template Pattern
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`,
},
};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`,
},
};Output Control
输出控制
javascript
// 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');
}
}javascript
// 强制结构化输出
const systemPrompt = `
Always respond with valid JSON in this format:
{
"title": "string",
"content": "string",
"suggestions": ["string"]
}
Never include any text outside the JSON.
`;
// 带降级处理的解析
function parseAIOutput(text) {
try {
return JSON.parse(text);
} catch {
// 降级:从响应中提取JSON
const match = text.match(/\{[\s\S]*\}/);
if (match) return JSON.parse(match[0]);
throw new Error('Invalid AI output');
}
}Quality Control
质量控制
| Technique | Purpose |
|---|---|
| Examples in prompt | Guide output style |
| Output format spec | Consistent structure |
| Validation | Catch malformed responses |
| Retry logic | Handle failures |
| Fallback models | Reliability |
undefined| 技术手段 | 目的 |
|---|---|
| 提示词中加入示例 | 引导输出风格 |
| 输出格式规范 | 保证结构一致性 |
| 验证机制 | 捕获格式错误的响应 |
| 重试逻辑 | 处理请求失败 |
| 备选模型 | 提升可靠性 |
undefinedCost Management
成本管理
Controlling AI API costs
When to use: When building profitable AI products
javascript
undefined控制AI API成本
适用场景:构建盈利性AI产品时
javascript
undefinedAI Cost Management
AI Cost Management
Token Economics
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;
}javascript
// 跟踪使用情况
async function callWithCostTracking(userId, prompt) {
const response = await anthropic.messages.create({...});
// 记录使用数据
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 }, // 每百万Token的费用
};
const rate = rates['claude-3-haiku'];
return (usage.input_tokens * rate.input +
usage.output_tokens * rate.output) / 1_000_000;
}Cost Reduction Strategies
成本削减策略
| Strategy | Savings |
|---|---|
| Use cheaper models | 10-50x |
| Limit output tokens | Variable |
| Cache common queries | High |
| Batch similar requests | Medium |
| Truncate input | Variable |
| 策略 | 节省幅度 |
|---|---|
| 使用更便宜的模型 | 10-50倍 |
| 限制输出Token数量 | 视情况而定 |
| 缓存常见查询 | 高 |
| 批量处理相似请求 | 中等 |
| 截断输入内容 | 视情况而定 |
Usage Limits
使用限制
javascript
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;
}undefinedjavascript
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;
}undefinedAnti-Patterns
反模式
❌ Thin Wrapper Syndrome
❌ 浅层封装综合征
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.
问题所在:缺乏差异化。用户直接使用ChatGPT即可,产品没有定价权,容易被复制。
改进方向:加入领域专业知识,针对特定任务优化用户体验,集成到工作流中,对输出进行后处理。
❌ Ignoring Costs Until Scale
❌ 规模扩大后才考虑成本
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.
问题所在:收到意外账单,单位经济效益为负,无法合理定价,业务无法持续。
改进方向:跟踪每一次API调用,了解每个用户的成本,设置使用限制,基于利润空间定价。
❌ No Output Validation
❌ 无输出验证
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.
问题所在:AI产生幻觉,格式不一致,用户体验差,失去用户信任。
改进方向:验证所有输出,解析结构化响应,设置降级处理,后处理保证一致性。
⚠️ Sharp Edges
⚠️ 注意事项
| 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 |
| 问题 | 严重程度 | 解决方案 |
|---|---|---|
| AI API成本失控 | 高 | ## 控制AI成本 |
| 达到API速率限制导致应用崩溃 | 高 | ## 处理速率限制 |
| AI提供错误或虚构信息 | 高 | ## 处理幻觉问题 |
| AI响应过慢影响用户体验 | 中 | ## 提升AI响应速度 |
Related Skills
相关技能
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