AI Wrapper Product
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.
Capabilities AI product architecture Prompt engineering for products API cost management AI usage metering Model selection AI UX patterns Output quality control AI product differentiation Patterns AI Product Architecture
Building products around AI APIs
When to use: When designing an AI-powered product
AI Product Architecture
The Wrapper Stack
User Input ↓ Input Validation + Sanitization ↓ Prompt Template + Context ↓ AI API (OpenAI/Anthropic/etc.) ↓ Output Parsing + Validation ↓ User-Friendly Response
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-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); }
Model Selection 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
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`,
},
};
Output Control
// 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'); } }
Quality Control Technique Purpose Examples in prompt Guide output style Output format spec Consistent structure Validation Catch malformed responses Retry logic Handle failures Fallback models Reliability
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; }
Cost Reduction Strategies Strategy Savings Use cheaper models 10-50x Limit output tokens Variable Cache common queries High Batch similar requests Medium Truncate input Variable Usage Limits 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