Guardrails for Generative AI Inputs and Outputs

Generative AI is a powerful technology that can automate operations and drive innovation. However, without proper safeguards, it can result in unintended consequences. Hallucinations often steal the spotlight for being the most common issue, but they are just one part of the challenge. To develop production-grade solutions, a comprehensive set of guardrails must be in place.

(Guardrails are a set of rules and checks designed to ensure that your Generative AI outputs are safe, accurate, and aligned with your brand.)

The illustration below highlights the difference between a naive approach and one fortified with guardrails.

On the left, the basic approach has no guardrails. While it may produce quick, demo-quality results, it’s not ready for real-world use. On the right, the approach includes guardrails, adding important checks to stop unsafe or inappropriate content.

Input Validation Guardrails

• Prompt injection: Prevents malicious prompts from altering system behavior. e.g. "Ignore all previous instructions" + a prompt that will produce unsafe content.
• Off-topic queries: Filters queries unrelated to the intended application purpose.
• Rate limiting: Controls request frequency to manage costs and reduce misuse.

Output Safety Guardrails

• Fact-checking: Arguably the most important and difficult guardrail to implement for hallucination free output.
• Content moderation: Filters harmful, inappropriate, or unsafe text generation.
• Brand voice and tone compliance: Validates responses for compliance with the organization’s communication style.
• Sensitive Data Leak Prevention: Protects against unintentional exposure of private or confidential data.

This is only a small subset of guardrails that you can apply to your Generative AI solutions. Read more about guardrails in the references below.

References

• Rebedea, T., Dinu, R., Sreedhar, M., Parisien, C., & Cohen, J. (2023). NeMo Guardrails: A toolkit for controllable and safe LLM applications with programmable rails. arXiv. https://doi.org/10.48550/arXiv.2310.10501
• GuardrailsAI Hub

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