GPT-4 in a Production Pipeline. Real Customers. Real Money. Not a Weekend Demo.

I put GPT-4 inside a DevOps pipeline. Full production -- beyond proof of concept, beyond weekend hackathon. Enterprise e-commerce. Real customers. Real money. How we integrated Azure OpenAI into the deployment pipeline - from intelligent code review to automated incident classification to natural language infrastructure queries. The technical details: how we handled rate limits, cost control, hallucination risks, and the one time the AI confidently suggested deleting a production database (we didn't). The pipeline handled four things: versioned prompt templates (you don't just push a prompt change to production), API gateway deployment with rate limiting and authentication, integration tests that validated response quality not just HTTP status codes, and canary releases with automatic rollback if the AI started giving wrong answers. KQL dashboards tracked token usage, response latency, error rates, and cost per request in real time. The result was a 60% reduction in AI deployment time - from manual, nerve-wracking releases to automated, observable, repeatable pipelines. Hard lessons from production: prompt changes are deployment changes - version them, test them, review them…