At 1:30 AM, my phone went crazy. The ops chat exploded: “The knowledge base QA endpoint is timing out — users are already cursing us.” I opened Grafana and saw P99 latency soaring to 34 seconds with a 40% error rate. I had confidently launched this LangChain‑based RAG system two weeks ago. The Colab demo ran buttery smooth, but moving it to production caused a total meltdown. Over the next eight hours, I peeled back LangChain’s elegant abstractions and uncovered three critical issues that can instantly kill your service.
Problem Breakdown: The Galaxy‑Sized Gap Between Demo and Production
Our use case is typical: ingest thousands of internal technical documents and runbooks into a vector store, then let engineers ask natural language questions — like “How to troubleshoot MySQL replication lag?” or “What are the steps to scale a Redis cluster?”
The pipeline is straightforward: user question → vector retrieval of relevant document chunks → prompt assembly → LLM generates an answer. In the local demo, with few docs and the model running in‑process, everything was peaceful.
Once in production, three problems hit us at once:
