AI technology is advancing at an astonishingly fast pace. Not only can AI systems carry on conversations and produce nearly any type of content, but they’re also becoming increasingly capable of acting on their own — without human intervention or explicit direction. AI agents are an example of this kind of independently acting AI tool.
While AI presents some incredible opportunities, it also poses enormous risks. Large language models (LLMs) — learning models that are trained on large datasets, interact with people through language, and power generative AI tools — have occasionally produced harmful, inappropriate, or incorrect outputs, prompting calls for action.
What can be done to manage LLMs and ensure users feel safe?
That’s where LLM guardrails come in. In this article, we’ll cover the basics of LLM guardrails, including what they are, what types of guardrails exist, why they’re important, and how to implement them.
What are LLM guardrails?
LLM guardrails are guidelines and constraints put in place to control the output of AI models, ensuring they operate within defined boundaries that adhere to predetermined ethical, legal, and operational standards.
“LLM guardrails act like the bumpers on a bowling lane — they keep the AI models on track,” says Arne Helgesen, an IT leader and cybersecurity expert at Sharecat. “Essentially, they’re rules and protocols designed to ensure that AI systems behave safely and ethically.”
The guardrails themselves are the various techniques used to maintain control. For example, engineers may incorporate rules into an AI system to guide it toward acceptable behavior. This could include directing it to provide “professional, unbiased” responses, for instance, or avoid giving information that would be in violation of a specific regulation.
Another method is content filtering. The addition of a content filtering system would block specific types of content — discriminatory language, for example — from being included in responses.
Other guardrail tactics include controlling the sources of information to minimize bias (bias mitigation guardrails), data masking (data privacy guardrails), and simply providing human oversight, particularly in situations where medical or legal advice is involved.
Why are LLM guardrails needed when building AI agents?
As a potentially hugely powerful technology, AI has the capacity to both do a lot of harm and do a lot of good. It’s the responsibility of developers to try to prevent harmful use and negative outcomes.
There’s also simply the practical importance of ensuring AI’s safety — when AI systems produce harmful or incorrect content, the result is an erosion of the public’s trust in the technology, and concerns about content with mistakes or misrepresentations will result in less use.
A bit of skepticism is justifiable, as some “off the rails” AI content has already made headlines. These incidents draw negative attention to the organizations involved.
“We need these guardrails because LLMs can sometimes surprise us with unexpected results,” says Helgeson. “Without them, AI outputs might be biased or even harmful, which can lead to problems like damaging a company’s reputation or breaking the law.”
Imagine if your company’s AI chatbot were to use offensive language with customers, for example, or cite incorrect information about your products or policies. AI hallucinations — incorrect results — can lead to poor customer service and lost money. In fact, this already happened to an Air Canada customer.
Adding guardrails is a positive step toward reducing these issues. Not only do they improve the output of AI systems and agents, but they also reflect positively on organizations that use them.
For Helgeson at Sharecat, “implementing guardrails means promoting responsible AI use and protecting our company’s and clients’ interests.”
What types of LLM guardrails are there?
There are several types of guardrails, each serving a different purpose:
- Ethical guardrails are designed to prevent outputs that could be discriminatory or harmful and promote outputs aligned with human values.
- Compliance guardrails ensure outputs meet relevant legal or industry standards. This is particularly important in regulated industries like finance or healthcare.
- Contextual guardrails help the AI understand what’s appropriate in specific situations and prevent it from providing misleading information.
- Security guardrails ensure the AI protects sensitive information, such as personally identifiable information or financial data.
- Adaptive guardrails evolve with the model to ensure the model keeps up with changing norms and regulations, such as new healthcare standards, so it stays compliant.
According to Helgesen, having one or more guardrails in place is like having a “multilayered security system around a valuable asset.” Integrating the appropriate guardrails into your AI projects can minimize the overall risk involved in user interactions and ensure optimal output.
How do you implement guardrails for LLMs?
Helgesen compares the process of implementing guardrails to building a fence — you need to follow concrete steps if you want the final product to be strong and effective.
“First, you identify why you need the fence — what are your goals? Next, you decide what kind of fence fits those goals — in our case, the types of guardrails. Then, you design the specifications — how tall should it be, how strong?
“After that, you actually build the fence by integrating the guardrails into the LLM. Once built, you test it to make sure it works as intended without slowing down the AI’s performance. Lastly, you keep an eye on the fence, making updates as necessary to ensure it continues to do its job effectively.”
Helgeson offers some best practices to make implementing guardrails more effective:
- Involve a variety of experts — like data scientists, engineers, and legal professionals — in the development process. Including professionals in a variety of disciplines helps catch potential blind spots and minimize bias in the AI system.
- Design with the user in mind. Where are typical conversations likely to lead, and what kind of information might they include? This is important to ensure the system remains engaging and safe.
- Conduct regular audits to help identify any gaps between expected and actual output. User feedback can also help you refine the guardrails based on real-world use.
- Provide thorough documentation and training to make sure everyone on the team knows how to implement and stick to these guardrail protocols effectively.
- Stay updated on evolving AI ethics standards and regulations to ensure compliance and learn about evolving best practices.
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