Building Production-Ready GPT Integrations: A Practical Guide to API Design and Error Handling

Your GPT-powered feature works perfectly in development. Users love the intelligent code suggestions, the natural language queries feel magical, and your demo goes flawlessly. Then you deploy to production. Within hours, rate limits trigger cascading failures across your application. Token costs spike to $847 in a single day because a nested loop you missed is making 10,000 API calls. Users complain about 30-second response times, and 12% of requests silently fail with cryptic 503 errors that your error handling never anticipated. This isn't a story about poor engineering—it's the reality of integrating LLM APIs into production systems. OpenAI's GPT endpoints look like standard REST APIs, complete with familiar HTTP methods and JSON responses. But beneath that familiar interface lies a fundamentally different beast. Traditional API integration patterns—the ones that work beautifully for Stripe, Twilio, or your internal microservices—become liabilities when applied to generative AI. The