From the course: Building Secure and Trustworthy LLMs Using NVIDIA Guardrails

Introduction to guardrails for LLMs

From the course: Building Secure and Trustworthy LLMs Using NVIDIA Guardrails

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Introduction to guardrails for LLMs

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- [Instructor] So, let's introduce you to the concept of guardrails which are essential for ensuring ethical and secure deployment of AI systems. So, what exactly are guardrails? Guardrails are specific ways to control the output of a large language model. For example, they can prevent discussions on sensitive topics like politics, ensure responses follow a predefined dialogue flow, or maintain a particular language style. This helps in creating trustworthy and safe AI interactions. To give you a clearer picture, imagine an application interacting with a large language model. The NeMo guardrails sit in between them, guiding and filtering the output to ensure it meets a desired criteria. This criteria will be set by us. This intermediary layer is what keeps the interactions secure and appropriate. Understanding and implementing guardrails is crucial for several reasons. They help us safeguard AI systems against misuse, enhance public trust, and ensure that AI interactions remain ethical and appropriate. As AI becomes more integrated into various aspects of our daily lives, these protections are more important than ever. So, let's look into some key protections that guardrails offer. First, they help us in building applications that are trustworthy, safe, and secure. Second, guardrails allow for secure connections between models and services, ensuring that the data flows seamlessly and securely, while abiding by a set of rules. Lastly, they provide protection against common vulnerabilities such as jailbreaks and prompt injection attacks. These attacks are prevalent in LLMs specifically. Guardrails are vital for deploying LLMs ethically and securely, and by implementing these programmable constraints, we can ensure that AI systems are used responsibly.

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