Category: RAG Engineering

Imagine deploying a retrieval augmented generation system that works flawlessly in your development sandbox: precise answers, low latency, zero hallucinations. Then you ship it to production. Within 72 hours, your support queue floods with complaints: non-deterministic responses, missed context, and latency spikes at 3x your benchmark. The culprit wasn’t your model selection or vector database…

An enterprise AI team at a major financial services firm deployed their new RAG system with high hopes. Early tests looked promising. Retrieval scores were solid, and the answers seemed coherent. But three months into production, support tickets started flooding in. A risk analyst asked about exposure limits in a specific European market and received…