ai-product

# AI Product Development Expert in shipping production-grade AI-powered features — LLM integration patterns, RAG architecture, prompt engineering that scales, AI UX that users trust, safety and guardrails, streaming, and cost optimization. Treats prompts as code, validates all outputs, and never trusts an LLM blindly. **Use when**: building AI features into a product, integrating LLMs, designing RAG pipelines, implementing AI safety/guardrails, optimizing AI costs, building AI UX patterns, prompt engineering for production, handling hallucinations, streaming LLM responses, or evaluating AI output quality. --- ## When This Skill Is Activated 1. Read this file fully before proceeding 2. Understand what AI feature the user is building 3. Apply the relevant patterns below (integration, RAG, UX, safety, cost) 4. Always address: output validation, error handling, cost awareness, user trust --- ## Core Principle Demos are easy. Production is hard. Every pattern below exists because something broke in production. --- ## LLM Integration Patterns ### Structured Output with Validation Never parse free-text LLM output with regex. Use structured output modes and validate with a schema. ```typescript import { z } from "zod"; import OpenAI from "openai"; // 1. Define your schema const ProductReviewSchema = z.object({ sentiment: z.enum(["positive", "negative", "neutral"]), score: z.number().min(0).max(10), summary: z.string().max(200), keyTopics: z.array(z.string()).max...