LLM Guardrails
to mitigate Hallucinations and high risk outputs
Avoiding hallucinations is critical to avoid LLM-induced risks.
Start from scratch, customize, or simply use a pre-existing library of guardrail templates for LLMs.
Put guardrails to avoid hallucinations
Ensure input/output guardrails are in place to avoid hallucination-induced risks.
Ensure LLMs' adherence to guidelines
Ensure your LLM apps edhere to guidelines and are not sending/receiving data it's not supposed to.
Model agnostic
Use a model of your choice. Put guardrails in place for every LLM you use.
Set up corrective next steps
Set up corrective steps for when LLM hallucinates.
