LLM Output Guardrails & Validation
Guardrails are programmable security rules that validate, filter, and sanitize both incoming prompts and outgoing AI responses before they reach the user.
LLM guardrails are programmatic pre- and post-processing validation layers designed to enforce domain boundaries, block toxic or adversarial content, and prevent data leakage.
How Guardrails works in practice
A pre-processing guardrail inspects incoming queries for jailbreaks, PII, and out-of-domain topics before calling the model.
A post-processing guardrail inspects the synthesized response to verify that it contains citations and does not violate predefined safety or legal constraints.
How SiteMind implements Guardrails
SiteMind features configurable guardrails including mandatory legal disclaimers, PII redaction, out-of-domain refusals, and custom tone constraints.
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Related Technical Concepts
Prompt Injection & Jailbreaking
Prompt injection is an adversarial attack where malicious user inputs attempt to override system instructions and hijack the AI’s behavior.
Source Grounding & Citations
Source grounding ensures every AI claim is directly supported by retrieved website text and includes clickable citations for instant human verification.
Enterprise Zero-Data Retention
Zero-data retention guarantees that customer chat transcripts and proprietary documents are never stored or used to train public foundation models.
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