Regtransfers/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.
• never 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.