How to Add NVIDIA NeMo Guardrails to a Production LLM Endpoint

This article provides a detailed tutorial on integrating NVIDIA NeMo Guardrails into a production LLM endpoint. It emphasizes placing guardrails in the enforced request path between the application and the LLM, not as an optional side service. The tutorial covers input, retrieval, execution, and output rails, using NVIDIA NIM as the inference backend. It also discusses production considerations such as fail-open/fail-closed decisions, tool handling, telemetry, adversarial testing, policy versioning, canary deployment, and rollback. The goal is to build a production-oriented pattern that blocks prohibited requests, enforces topic boundaries, inspects context, and validates outputs, ensuring safe and governed LLM interactions.