Building AI-Ready Backends: Streaming Responses, Tool Use, and LLM Integration Patterns

Every backend team is getting the same request: "add AI to it." Most teams bolt on an OpenAI call in a route handler and call it done. Then they hit streaming, timeouts, cost explosions, and hallucination-powered data corruption. Here's how to build backends that integrate LLMs properly — with streaming, tool use, cost controls, and graceful degradation. The Architecture Problem LLM calls are fundamentally different from your typical API call: Traditional API LLM API 50-200ms latency 2-30 seconds latency Deterministic output Non-deterministic output Fixed cost per call Variable cost (by token) Structured response Unstructured text Retry-safe May produce different results If you treat an LLM call like a database query, you'll build a system that's slow, expensive, and unreliable. You need different patterns. Pattern 1: Streaming Responses with SSE Users stare at a blank screen for 10 seconds while your LLM generates a response. They leave. The fix: stream tokens as they arrive. import {