ai-wrapper-product

AI Wrapper Product

Expert in building products that wrap AI APIs (OpenAI, Anthropic, etc.) into focused tools people will pay for. Not just "ChatGPT but different" - products that solve specific problems with AI. Covers prompt engineering for products, cost management, rate limiting, and building defensible AI businesses.

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.

Expertise

• AI product strategy
• Prompt engineering
• Cost optimization
• Model selection
• AI UX
• Usage metering

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),
    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;
}

AI Product Differentiation

Standing out from other AI wrappers

When to use: When planning AI product strategy

AI Product Differentiation

What Makes AI Products Defensible

Moat	Example
Workflow integration	Email inside Gmail
Domain expertise	Legal AI with law training
Data/context	Company-specific knowledge
UX excellence	Perfectly designed for task
Distribution	Built-in audience

Differentiation Strategies

1. Vertical Focus
   Generic: "AI writing assistant"
   Specific: "AI for Amazon product descriptions"

2. Workflow Integration
   Standalone: Web app
   Integrated: Chrome extension, Slack bot

3. Domain Training
   Generic: Uses raw GPT
   Specialized: Fine-tuned or RAG-enhanced

4. Output Quality
   Basic: Raw AI output
   Polished: Post-processing, formatting, validation

Avoid "Thin Wrappers"

Thin Wrapper	Real Product
ChatGPT with custom prompt	Domain-specific workflow tool
API passthrough	Processed, validated outputs
Single feature	Complete solution
No unique value	Solves specific pain point

Sharp Edges

AI API costs spiral out of control

Severity: HIGH

Situation: Monthly AI bill is higher than revenue

Symptoms:

• Surprise API bills
• Costs > revenue
• Rapid usage spikes
• No visibility into costs

Why this breaks: No usage tracking. No user limits. Using expensive models. Abuse or bugs.

Recommended fix:

Controlling AI Costs

Set Hard Limits

// Per-user limits
const LIMITS = {
  free: { dailyCalls: 10, monthlyTokens: 50000 },
  pro: { dailyCalls: 100, monthlyTokens: 500000 },
};

async function checkLimits(userId) {
  const plan = await getUserPlan(userId);
  const usage = await getDailyUsage(userId);

  if (usage.calls >= LIMITS[plan].dailyCalls) {
    throw new Error('Daily limit reached');
  }
}

Provider-Level Limits

OpenAI: Set usage limits in dashboard
Anthropic: Set spend limits
Add alerts at 50%, 80%, 100%

Cost Monitoring

// Alert on anomalies
async function checkCostAnomaly() {
  const todayCost = await getTodayCost();
  const avgCost = await getAverageDailyCost(30);

  if (todayCost > avgCost * 3) {
    await alertAdmin('Cost anomaly detected');
  }
}

Emergency Shutoff

// Kill switch
const MAX_DAILY_SPEND = 100; // $100

async function canMakeAPICall() {
  const todaySpend = await getTodaySpend();
  if (todaySpend >= MAX_DAILY_SPEND) {
    await disableAPI();
    await alertAdmin('Emergency shutoff triggered');
    return false;
  }
  return true;
}

App breaks when hitting API rate limits

Severity: HIGH

Situation: API calls fail with 429 errors

Symptoms:

• 429 Too Many Requests errors
• Requests failing in bursts
• Users seeing errors
• Inconsistent behavior

Why this breaks: No retry logic. Not queuing requests. Burst traffic not handled. No backoff strategy.

Recommended fix:

Handling Rate Limits

Retry with Exponential Backoff

async function callWithRetry(fn, maxRetries = 3) {
  for (let i = 0; i < maxRetries; i++) {
    try {
      return await fn();
    } catch (err) {
      if (err.status === 429 && i < maxRetries - 1) {
        const delay = Math.pow(2, i) * 1000; // 1s, 2s, 4s
        await sleep(delay);
        continue;
      }
      throw err;
    }
  }
}

Request Queue

import PQueue from 'p-queue';

// Limit concurrent requests
const queue = new PQueue({
  concurrency: 5,
  interval: 1000,
  intervalCap: 10, // Max 10 per second
});

async function callAPI(prompt) {
  return queue.add(() => anthropic.messages.create({...}));
}

User-Facing Handling

try {
  const result = await callWithRetry(generateContent);
  return result;
} catch (err) {
  if (err.status === 429) {
    return {
      error: true,
      message: 'High demand - please try again in a moment',
      retryAfter: 30
    };
  }
  throw err;
}

AI gives wrong or made-up information

Severity: HIGH

Situation: Users complain about incorrect outputs

Symptoms:

• Users report wrong information
• Made-up facts in outputs
• Outdated information
• Trust issues

Why this breaks: No output validation. Trusting AI blindly. No fact-checking. Wrong use case for AI.

Recommended fix:

Handling Hallucinations

Output Validation

function validateOutput(output, schema) {
  // Check required fields
  if (!output.title || !output.content) {
    throw new Error('Missing required fields');
  }

  // Check reasonable length
  if (output.content.length < 50 || output.content.length > 5000) {
    throw new Error('Content length out of range');
  }

  // Check for placeholder text
  const placeholders = ['[INSERT', 'PLACEHOLDER', 'YOUR NAME HERE'];
  if (placeholders.some(p => output.content.includes(p))) {
    throw new Error('Output contains placeholders');
  }

  return true;
}

Domain-Specific Validation

// For factual content
async function validateFacts(output) {
  // Check dates are reasonable
  const dates = extractDates(output);
  for (const date of dates) {
    if (date > new Date() || date < new Date('1900-01-01')) {
      return { valid: false, reason: 'Suspicious date' };
    }
  }

  // Check numbers are reasonable
  // ...
}

Use Cases to Avoid

Risky	Safer Alternative
Medical advice	Summarize, not diagnose
Legal advice	Draft, not advise
Current events	Use with data sources
Precise calculations	Validate or use code

User Expectations

• Disclaimer for generated content
• "AI-generated" labels
• Edit capability for users
• Feedback mechanism

AI responses too slow for good UX

Severity: MEDIUM

Situation: Users complain about slow responses

Symptoms:

• Long wait times
• Users abandoning
• Timeout errors
• Poor perceived performance

Why this breaks: Large prompts. Expensive models. No streaming. No caching.

Recommended fix:

Improving AI Latency

Streaming Responses

// Stream to user as AI generates
async function* streamResponse(prompt) {
  const stream = await anthropic.messages.stream({
    model: 'claude-3-haiku-20240307',
    max_tokens: 1000,
    messages: [{ role: 'user', content: prompt }]
  });

  for await (const event of stream) {
    if (event.type === 'content_block_delta') {
      yield event.delta.text;
    }
  }
}

// Frontend
const response = await fetch('/api/generate', { method: 'POST' });
const reader = response.body.getReader();
while (true) {
  const { done, value } = await reader.read();
  if (done) break;
  appendToOutput(new TextDecoder().decode(value));
}

Caching

async function generateWithCache(prompt) {
  const cacheKey = hashPrompt(prompt);
  const cached = await cache.get(cacheKey);
  if (cached) return cached;

  const result = await generateContent(prompt);
  await cache.set(cacheKey, result, { ttl: 3600 });
  return result;
}

Use Faster Models

Model	Typical Latency
GPT-4	5-15s
GPT-4o-mini	1-3s
Claude 3 Haiku	1-3s
Claude 3.5 Sonnet	2-5s

Validation Checks

AI API Key Exposed

Severity: HIGH

Message: AI API key may be exposed - security risk!

Fix action: Move API calls to backend, use environment variables

No AI Usage Tracking

Severity: HIGH

Message: Not tracking AI usage - cost control issue.

Fix action: Log tokens and costs for every API call

No AI Error Handling

Severity: HIGH

Message: AI errors not handled gracefully.

Fix action: Add try/catch, retry logic, and user-friendly error messages

No AI Output Validation

Severity: MEDIUM

Message: Not validating AI outputs.

Fix action: Add output parsing, validation, and error handling

No Response Streaming

Severity: LOW

Message: Not using streaming - could improve UX.

Fix action: Implement streaming for better perceived performance

Collaboration

Delegation Triggers

• prompt engineering|advanced LLM|fine-tuning -> llm-architect (Advanced AI patterns)
• SaaS|pricing|launch|business -> micro-saas-launcher (AI product business)
• frontend|UI|react -> frontend (AI product interface)
• backend|API|database -> backend (AI product backend)
• browser extension -> browser-extension-builder (AI browser extension)
• telegram bot -> telegram-bot-builder (AI telegram bot)

AI Writing Tool

Skills: ai-wrapper-product, frontend, micro-saas-launcher

Workflow:

1. Define specific writing use case
2. Design prompt templates
3. Build UI with streaming
4. Add usage tracking and limits
5. Implement payments
6. Launch and iterate

AI Browser Extension

Skills: ai-wrapper-product, browser-extension-builder

Workflow:

1. Define AI-powered feature
2. Build extension structure
3. Integrate AI API via backend
4. Add usage limits
5. Publish to Chrome Store

AI Telegram Bot

Skills: ai-wrapper-product, telegram-bot-builder

Workflow:

1. Define bot personality/purpose
2. Build Telegram bot
3. Integrate AI for responses
4. Add monetization
5. Launch and grow

Related Skills

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

When to Use

• User mentions or implies: AI wrapper
• User mentions or implies: GPT product
• User mentions or implies: AI tool
• User mentions or implies: wrap AI
• User mentions or implies: AI SaaS
• User mentions or implies: Claude API product

Limitations

• Use this skill only when the task clearly matches the scope described above.
• Do not treat the output as a substitute for environment-specific validation, testing, or expert review.
• Stop and ask for clarification if required inputs, permissions, safety boundaries, or success criteria are missing.