Implement custom input/output guardrails for your LLM applications

LLM Guardrails

to mitigate Hallucinations and high risk outputs

Set custom guardrails for your LLM application

Set custom guardrails for your LLM application

Avoiding hallucinations is critical to avoid LLM-induced risks.

Use Pre-existing templates

Use Pre-existing templates

Use Pre-existing templates

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.

Application Security for Generative AI

arnav@layerupai.com

+1-650-753-8947

Subscribe to stay up to date with an LLM cybersecurity newsletter:

Application Security for Generative AI

arnav@layerupai.com

+1-650-753-8947

Subscribe to stay up to date with an LLM cybersecurity newsletter:

Application Security for Generative AI

arnav@layerupai.com

+1-650-753-8947

Subscribe to stay up to date with an LLM cybersecurity newsletter: