The original title is: "RAG Is Not a Chatbot Feature. It Is Production AI Infrastructure."

Short summary

Most enterprise RAG failures stem from infrastructure gaps, not model limitations—demos work on clean data with simple permissions because nobody measures drift, latency, or hallucination risk. Production RAG demands data pipelines, identity-aware retrieval, source quality scoring, prompt guardrails, inference cost controls, comprehensive observability, and human approval for sensitive actions. The real question is not which LLM to use, but what infrastructure makes AI answers trustworthy enough for business deployment.

• •RAG failures are infrastructure failures, not model failures
• •Production systems require data pipelines, identity controls, quality scoring, guardrails, cost governance, and observability
• •Infrastructure readiness matters more than LLM selection for enterprise deployment

Generated with AI, which can make mistakes.