nemo-guardrails

Prevent prompt injection attacks by implementing runtime guardrails that actively detect and block jailbreak attempts. NeMo Guardrails provides programmable security rails using Colang 2.0 DSL to validate interactions and refuse malicious prompts.

Yes, you can filter PII at runtime using input and output validation rails. The framework masks or removes Personally Identifiable Information from conversations, ensuring your customer-facing chatbot does not reveal sensitive data.

Detect hallucinations programmatically by integrating retrieval mechanisms for fact-checking. The framework verifies factual claims during runtime validation, reducing hallucinations and ensuring factual accuracy in LLM outputs.

Yes, NeMo Guardrails supports production deployment with GPU acceleration. It implements runtime safety and security rails designed to scale, protecting LLM applications from harmful outputs and misuse in production environments.

The best way to add safety rails is implementing programmable validation checks for toxicity, PII, and factual accuracy. This framework secures chatbots from generating inappropriate content or responding to malicious prompts.

Yes, you need to learn Colang 2.0 DSL to define programmable guardrails for LLM security. This domain-specific language is used to write rules for runtime validation, jailbreak detection, and safety alignment.