Most enterprise RAG failures are not model failures.
They are infrastructure failures.
The demo works because the PDF is clean, the user is friendly, the permissions are simple, and nobody is measuring drift, latency, access control, source quality, or hallucination risk.
Production RAG needs more than a vector database:
- Data pipelines that know what changed
- Identity-aware retrieval
- Source quality scoring
- Prompt and response guardrails
- GPU / inference cost controls
- Observability for retrieval, latency, grounding, and failed answers
- Human approval for high-risk actions
The real question is not:
Which LLM should we use?
The better question is:
What infrastructure makes this AI answer trustworthy enough for business use?
Discussion question:
If you were building an enterprise RAG system today, which layer would you harden first: data quality, access control, evaluation, observability, or cost governance?
Tags: Enterprise AI, RAG, LLMOps, Cloud Architecture, AI Infrastructure, MLOps, Responsible AI, Generative AI.