nemo-guardrails

Runtime safety rails are added to LLM applications by applying programmable input/output validation, jailbreak detection, and PII filtering at runtime. This enforces policy-based checks to reduce the risk of unsafe prompts, data leakage, and harmful outputs.

Detecting jailbreak attempts in production LLMs is best handled by programmable runtime safety rails that identify and block prompt injection efforts. This approach prevents malicious actors from bypassing established model safeguards.

PII filtering for large language model prompts and responses works by redacting sensitive information before processing or logging occurs. Runtime safety rails enforce this programmatically, ensuring personally identifiable information is stripped from production conversations.

Yes, you need a GPU to run Colang 2.0 guardrails for hallucination detection reliably. The runtime safety rails require GPU-accelerated environments, such as NVIDIA T4 setups, to process fact-checking and validation checks.

Yes, you can perform fact-checking and retrieval validation on LLM outputs at runtime. Programmable safety rails verify factual claims and optionally fetch authoritative information to ensure production responses are accurate.

Limitations of programmable safety rails for LLM input validation include the strict requirement for GPU-accelerated environments. Without adequate hardware like NVIDIA T4 GPUs, the runtime fact-checking and jailbreak detection processes cannot operate reliably.