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

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)