Safeguarding LLMs with Guardrails

As the use of large language model (LLM) applications enters the mainstream and expands into larger enterprises, there is a distinct need to establish effective governance of productionized applications. Given that the open-ended nature of LLM-driven applications can produce responses that may not align with an organization’s guidelines or policies, a set of safety measurements and actions are becoming table stakes for maintaining trust in generative AI. This guide is designed to walk you through several available frameworks and how to think through implementation. <h1>What Are LLM Guardrails?</h1> Guardrails are the set of safety controls that monitor and dictate a user’s interaction with a LLM application. They are a set of programmable, rule-based systems that sit in between users and foundational models in order to make sure the AI model is operating between defined principles in an organization. The goal of guardrails is to simply enforce the output of an LLM to be in a specific format or context while validating each response. By implementing guardrails, users can define structure, type, and quality of LLM responses. Let’s look at a simple example of an LLM dialogue with and without guardrails: <a href="https://towardsdatascience.com/safeguarding-llms-with-guardrails-4f5d9f57cff2">Visit Now</a>