Guardrails

Mon, 01 Jan 0001 00:00:00 +0000

Guardrails are controls wrapped around LLM inference to reduce harmful, non-compliant, or off-policy behavior without replacing the base model. Their objective is AI safety and governance in production: block or rewrite prompts that attempt prompt injection or jailbreaks, filter toxic or leaked PII in outputs, enforce topic allowlists, validate structured tool calls, and log decisions for audit. Guardrails sit on the request path (before tokens reach the model or after the model proposes a draft response), combining rule engines, classifiers, regex, and sometimes smaller models. They complement—not replace—application auth, network policy, and human review; enterprises treat them as mandatory for customer-facing and internal copilots.

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Guardrails