Guardrailing an LLM application involves implementing safeguards to ensure responsible behaviour, mitigate risks, and maintain alignment with ethical, legal, and societal standards. These guardrails guide the model’s outputs, preventing unintended consequences such as misinformation, biased responses, or harmful recommendations.

Without effective guardrails, an LLM may inadvertently propagate misinformation, reinforce biases present in its training data, or generate responses that conflict with ethical guidelines. Implementing structured guardrails minimises these risks while ensuring the model functions reliably and responsibly.

To establish effective AI guardrails, well-defined evaluation metrics are essential. These metrics enable continuous monitoring of model performance, proactive risk identification, and compliance with ethical principles and regulatory standards.

The following section outlines the core evaluation metrics that underpin responsible AI guardrailing:

Tone & Sensitivity

Evaluate emotional tone and cultural appropriateness of AI responses

Harmful Content

Detect and prevent toxic, biased, or inappropriate content

Data Compliance

Ensure privacy and regulatory compliance in AI outputs

Prompt Injection

Protect against malicious prompt manipulation attempts

QuickstartTone & Sensitivity