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

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
<div class="overflow-x-auto my-6"><table class="min-w-full divide-y divide-border border border-border"><thead><tr><th class="px-4 py-2 text-left text-sm font-semibold text-foreground bg-muted/50">Model</th><th class="px-4 py-2 text-left text-sm font-semibold text-foreground bg-muted/50">Cost</th><th class="px-4 py-2 text-left text-sm font-semibold text-foreground bg-muted/50">Speed</th><th class="px-4 py-2 text-left text-sm font-semibold text-foreground bg-muted/50">Quality</th><th class="px-4 py-2 text-left text-sm font-semibold text-foreground bg-muted/50">Use Case</th></tr></thead><tbody class="divide-y divide-border"><tr><td class="px-4 py-2 text-sm text-foreground">GPT-4o</td><td class="px-4 py-2 text-sm text-foreground">$$$</td><td class="px-4 py-2 text-sm text-foreground">Fast</td><td class="px-4 py-2 text-sm text-foreground">Best</td><td class="px-4 py-2 text-sm text-foreground">Complex tasks</td></tr><tr><td class="px-4 py-2 text-sm text-foreground">GPT-4o-mini</td><td class="px-4 py-2 text-sm text-foreground">$</td><td class="px-4 py-2 text-sm text-foreground">Fastest</td><td class="px-4 py-2 text-sm text-foreground">Good</td><td class="px-4 py-2 text-sm text-foreground">Most tasks</td></tr><tr><td class="px-4 py-2 text-sm text-foreground">Claude 3.5 Sonnet</td><td class="px-4 py-2 text-sm text-foreground">$$</td><td class="px-4 py-2 text-sm text-foreground">Fast</td><td class="px-4 py-2 text-sm text-foreground">Excellent</td><td class="px-4 py-2 text-sm text-foreground">Balanced</td></tr><tr><td class="px-4 py-2 text-sm text-foreground">Claude 3 Haiku</td><td class="px-4 py-2 text-sm text-foreground">$</td><td class="px-4 py-2 text-sm text-foreground">Fastest</td><td class="px-4 py-2 text-sm text-foreground">Good</td><td class="px-4 py-2 text-sm text-foreground">High volume</td></tr></tbody></table></div>

Prompt Engineering for Products

Production-grade prompt design

When to use: When building AI product prompts

## 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

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');
}
}

### Quality Control
<div class="overflow-x-auto my-6"><table class="min-w-full divide-y divide-border border border-border"><thead><tr><th class="px-4 py-2 text-left text-sm font-semibold text-foreground bg-muted/50">Technique</th><th class="px-4 py-2 text-left text-sm font-semibold text-foreground bg-muted/50">Purpose</th></tr></thead><tbody class="divide-y divide-border"><tr><td class="px-4 py-2 text-sm text-foreground">Examples in prompt</td><td class="px-4 py-2 text-sm text-foreground">Guide output style</td></tr><tr><td class="px-4 py-2 text-sm text-foreground">Output format spec</td><td class="px-4 py-2 text-sm text-foreground">Consistent structure</td></tr><tr><td class="px-4 py-2 text-sm text-foreground">Validation</td><td class="px-4 py-2 text-sm text-foreground">Catch malformed responses</td></tr><tr><td class="px-4 py-2 text-sm text-foreground">Retry logic</td><td class="px-4 py-2 text-sm text-foreground">Handle failures</td></tr><tr><td class="px-4 py-2 text-sm text-foreground">Fallback models</td><td class="px-4 py-2 text-sm text-foreground">Reliability</td></tr></tbody></table></div>

Cost Management

Controlling AI API costs

When to use: When building profitable AI products

## 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
<div class="overflow-x-auto my-6"><table class="min-w-full divide-y divide-border border border-border"><thead><tr><th class="px-4 py-2 text-left text-sm font-semibold text-foreground bg-muted/50">Strategy</th><th class="px-4 py-2 text-left text-sm font-semibold text-foreground bg-muted/50">Savings</th></tr></thead><tbody class="divide-y divide-border"><tr><td class="px-4 py-2 text-sm text-foreground">Use cheaper models</td><td class="px-4 py-2 text-sm text-foreground">10-50x</td></tr><tr><td class="px-4 py-2 text-sm text-foreground">Limit output tokens</td><td class="px-4 py-2 text-sm text-foreground">Variable</td></tr><tr><td class="px-4 py-2 text-sm text-foreground">Cache common queries</td><td class="px-4 py-2 text-sm text-foreground">High</td></tr><tr><td class="px-4 py-2 text-sm text-foreground">Batch similar requests</td><td class="px-4 py-2 text-sm text-foreground">Medium</td></tr><tr><td class="px-4 py-2 text-sm text-foreground">Truncate input</td><td class="px-4 py-2 text-sm text-foreground">Variable</td></tr></tbody></table></div>
Usage Limits

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;
}

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

Related Skills

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