Build Profitable AI-Powered Products

This skill acts as an AI Product Architect, building products where AI is the engine rather than the gimmick - treating prompt engineering as product development and balancing API cost against user experience. It covers AI product architecture, prompt engineering for products, API cost management, usage metering, model selection, AI UX patterns, output quality control, and product differentiation.

The wrapper stack it recommends flows from user input through validation and sanitization, a prompt template plus context, the AI API call, output parsing and validation, and finally a user-friendly response. A basic implementation validates input length, builds a system prompt from role/format/tone context, calls the model, then parses and validates the output:

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 weighs cost, speed, and quality across options - a top-tier model for complex tasks, a cheaper/faster variant for most tasks, a balanced mid-tier option, and the cheapest/fastest option for high-volume use. For output control it forces structured JSON responses via explicit format instructions, with a regex fallback to extract JSON if the model wraps it in extra text. Cost management tracks per-call token usage and cost by user for billing and analysis, applies reduction strategies (cheaper models, limiting output tokens, caching common queries, batching, truncating input), and enforces monthly usage limits per user that throw once a cost ceiling is reached.

Three anti-patterns are called out explicitly: a thin wrapper with no differentiation that users can replicate with raw ChatGPT (fix: add domain expertise, perfect the UX for a specific task, integrate into workflows, post-process outputs); ignoring costs until scale, leading to surprise bills and unviable unit economics (fix: track every API call, know cost per user, set usage limits, price with margin); and skipping output validation, leading to hallucinations and inconsistent formatting (fix: validate outputs, parse structured responses, add fallback handling). It also flags recurring sharp edges - runaway API costs, breaking on rate limits, hallucinated information, and latency hurting UX - each pointing to a dedicated mitigation.

Use this skill when designing an AI-powered product, building production-grade AI product prompts, or building a profitable AI product that needs real cost discipline. It works well alongside llm-architect, micro-saas-launcher, frontend, and backend skills.

Output is working product code (prompt templates, API call wrappers, cost tracking, usage limits) plus a model-selection and cost-reduction strategy matched to the product's task and volume.

Founders and product engineers building AI-powered products on top of LLM APIs who need real differentiation, disciplined cost management, and reliable output quality rather than a thin, easily-replicated wrapper.