LLM integrations look simple in demos. In production, they fail in ways most tutorials never cover. What you will learn in this guide: How to structure an LLM integration that does not block your API How to implement streaming responses for better user experience How to manage token costs before they become a budget problem How to version prompts without redeploying your application How to build graceful fallback when the AI call fails or times out The Problem With Most AI Integration Tutorials Most LLM integration guides stop at "call the API, log the response." That is enough to demo. It is not enough to ship. Production AI features fail in ways that do not appear in tutorials: the LLM API times out under load, a prompt that worked in testing generates hallucinated output with slightly different user input, token costs spike 10x when a user submits a longer-than-expected document, and the entire feature breaks every other feature when the AI call blocks the event loop.…