nemo-guardrails

Runtime guardrails for LLM applications are added by wrapping your model with LLMRails using a RailsConfig defined by Colang 2.0 DSL to enforce safety flows. This setup provides programmable input validation, jailbreak detection, and PII filtering for production chatbots.

Programmable safety rails are runtime checks that enforce policy and prevent unsafe LLM outputs. You need them in production chatbots, virtual assistants, or enterprise AI agents requiring configurable input validation, toxicity detection, and fact-checking to reduce jailbreak risks.

You configure multi-layer safety flows by defining rules in Colang 2.0 DSL within a RailsConfig, then initializing LLMRails with that configuration. This programmable approach enforces fact-checking, toxicity detection, and input validation to keep interactions safe.

Yes, you must install the nemoguardrails package separately to use this Skill. Setting up runtime safety checks requires installing nemoguardrails, then wrapping your LLM with LLMRails using a RailsConfig to apply the desired safety flows.

Yes, runtime guardrails support enterprise AI agents by providing configurable fact-checking, toxicity detection, and PII filtering. Using Colang 2.0 DSL, you can define multi-layer safety flows to enforce policy and reduce unsafe outputs in production deployments.

The best way to prevent jailbreaks in production LLM chatbots is implementing programmable runtime guardrails. By configuring LLMRails with Colang 2.0 DSL, you enforce input validation, output checks, and fact-checking to systematically reduce jailbreak risks and unsafe outputs.