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 ```python ## 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 | 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 ```javascript ## 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 | 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 ```javascript ## 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 | Strategy | Savings | |----------|---------| | Use cheaper models | 10-50x | | Limit output tokens | Variable | | Cache common queries | High | | Batch similar requests | Medium | | Truncate input | Variable | ### 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 | 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` --- > Converted and distributed by [TomeVault](https://tomevault.io) | [Claim this content](https://tomevault.io/claim/davila7/claude-code-templates) <!-- tomevault:2.0:skill_md:2026-04-05 -->