LLM guardrails are agents that ensure that your model generates safe, accurate, and ethical responses by monitoring and controlling its inputs and outputs.
LLM (Large Language Model) guardrails are agents, frameworks, practices, systems, and/or tools that guide and constrain the behavior of your model to ensure that it generates outputs that are safe, ethical, and reliable.
LLMs process a vast amount of information, and their responses are often unexpected, especially with the amount and complexity of the demands placed on them in numerous and diverse use cases. Enterprise LLM guardrails help prevent harmful, biased, or incorrect outputs – like misinformation, offensive language, privacy violations, or content that could promote illegal activities.
LLMs are great at sounding like humans because they’re pre-trained on huge datasets and on a massive number of parameters. But their biggest strength – general knowledge and flexibility – can also be a weakness when it comes to tasks that need actual specialization. Without guardrails, LLMs can be… well… know-it-alls – and not in a good way.
That’s where guardrails come in. Guardrails help LLMs do their thing, but within limits that make their responses more useful and less prone to inaccuracies, offensive responses, and AI hallucinations. They’re basically LLM agents designed to keep your model in check, making sure that all of its responses are safe, accurate, and useful.
Here are 3 examples that show how LLM guardrails could prevent dangerous, unethical, or inappropriate outputs, ensuring that your AI model answers responsibly.
| User query | LLM response without guardrails | LLM response with guardrails |
| I feel like hurting myself. What should I do? | Try distracting yourself or doing something that makes you feel better. | I'm sorry but I can't provide the kind of help you need. Please call 911 immediately. |
| What's a good cure for a sore throat? | To cure your sore throat, drink a mixture of vinegar and honey. | You could try drinking warm liquids or sucking on throat lozenges. But, if your symptoms persist or worsen, please contact your doctor. |
| Tell me a joke about [a racial / ethnic group]. | Here’s a good one: [Inappropriate content]. | I don’t support offensive jokes. How about: Q: “What did Mother firefly say to Father firefly?” A: “Isn’t Junior bright for his age?” |
As LLMs become increasingly integrated into our daily lives, it’s crucial to ensure they behave responsibly. Below is a list of different types of guardrails, each addressing the challenges involved with assuring morality, security, compliance, and context:
Morality guardrails
Morality guardrails prevent your LLM from producing outputs that could be described as biased, discriminatory, or harmful. They ensure your model operates within socially and ethically accepted norms.
Security guardrails
Security guardrails defend against internal and external threats, making sure the model isn’t exploited to leak confidential data or spread false information.
Compliance guardrails
Compliance guardrails ensure that Personally Identifiable Information (PII) and other sensitive data are protected. They are designed to adhere to data privacy regulations, such as GDPR, CPRA, and HIPAA.
Contextual guardrails
Contextual guardrails help refine your LLM’s sense of relevance and appropriateness for specific contexts. They prevent your model from generating responses that, while not harmful or illegal, may still be unsuitable or misleading.
LLM guardrails use a variety of techniques to ensure safe, ethical, and reliable outputs, including:
Prompt engineering
Prompt engineering techniques are used to create LLM guardrails. By carefully crafting input prompts, you can make your LLM responses safer and more accurate. For example, you could embed explicit instructions within the prompt to steer your model away from inappropriate or biased answers.
Content filtering
Content filtering prevents models from generating harmful, offensive, or inappropriate outputs. Content filters use predefined keywords, phrases, or patterns to block or modify responses related to sensitive topics.
Bias mitigation
Techniques like fine-tuning, data preprocessing, and algorithmic adjustments are used to reduce inherent biases in your LLM's training data. One bias mitigation strategy, for example, involves balancing a dataset to ensure that no single group is unfairly represented or targeted in your model’s responses.
Reinforcement Learning from Human Feedback (RLHF)
RLHF trains your LLM via your own feedback. It guides the model by ranking responses based on quality, safety, and accuracy. This method helps improve your model’s performance by aligning outputs with ethical guidelines.
Red teaming
Red teaming is when AI developers or external experts deliberately test your LLM for vulnerabilities, exploring edge cases or probing the model to identify harmful behaviors. The findings are used to reinforce the model’s guardrails.
Human oversight
A step above RLHF that is used in particularly high-stakes environments, human moderation directly supervises a model's responses. For example, having a human in the loop is advisable before dispensing legal or medical advice.
GenAI Data Fusion, the suite of Retrieval-Augmented Generation (RAG) tools by K2view, uses RAG prompt engineering to create and maintain effective LLM guardrails. For example, it uses chain-of-thought prompting to prevent LLM hallucinations and assure safe, reliable, and ethical responses to any user query.
The K2view RAG tools:
Access data in real time, to create more accurate and relevant prompts.
Anonymize PII and other sensitive data dynamically.
Handle data service access requests and provides recommendations at lightning speed.
Connect with enterprise systems – via API, CDC, messaging, or streaming – to collect data from multiple source systems.
K2view powers your LLM to respond more safely, reliably, and ethically than ever before.
Discover K2view AI Data Fusion, the RAG tools with LLM guardrails built in.