LLM Integration Patterns That Don’t Break in Production
Streaming, retries, observability, and cost control when wiring OpenAI and custom models into real products.
🤖 AI Demos Are Easy, Production Is Not Calling an LLM API is simple. Building a system users can rely on is not. Production-grade AI requires reliability, monitoring, and cost awareness.
⚡ Streaming Improves UX Streaming responses make your app feel fast. But partial responses must be handled carefully—broken streams destroy user trust instantly.
🔁 Retries & Failure Handling Failures will happen. Always implement retries with exponential backoff. More importantly, show users what’s happening instead of failing silently.
💰 Cost नियंत्रण is Critical LLMs are powerful—but expensive. Track token usage per request. Without visibility, your costs can explode overnight.
🧾 Prompt Versioning Treat prompts like code. Version them, track changes, and keep history. When something breaks, you’ll need to know why.
📊 Observability Wins Log everything: inputs, outputs, latency, failures. Without observability, debugging AI systems becomes guesswork.
🏁 Final Thought Smart AI systems are not just intelligent—they are reliable, observable, and cost-efficient.