ai-wrapper-product

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

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

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

## Prompt Engineering for Products

### Prompt Template Pattern

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

## AI Cost Management

### Token Economics

// 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