AI Guardrail Rule Tester
No rules defined
Load a preset pack or add custom rules
Actions
What This Tool Does
AI Guardrail Rule Tester is built for deterministic developer and agent workflows.
Build and test AI guardrail rules with instant feedback — preset PII, injection, and safety patterns.
Use How to Use for execution steps and FAQ for constraints, policies, and edge cases.
Last updated:
This tool is provided as-is for convenience. Output should be verified before use in any production or critical context.
Agent Invocation
Best Path For Builders
Browser workflow
Runs instantly in the browser with private local processing and copy/export-ready output.
Browser Workflow
This tool is optimized for instant in-browser execution with local data handling. Run it here and copy/export the output directly.
/guardrail-rule-tester/
For automation planning, fetch the canonical contract at /api/tool/guardrail-rule-tester.json.
How to Use AI Guardrail Rule Tester
- 1
Build a rule to block harmful outputs
Create rule: pattern='jailbreak attempt' OR intent='ignore_instructions'. Add trigger words to match. Test against candidate outputs. If matched, rule blocks it. Use for content policy enforcement.
- 2
Test guardrail rules against real LLM outputs
Generate sample outputs from your LLM, paste into tester. Run each guardrail rule. See which rules trigger, why, and what to adjust. Catch false positives before production.
- 3
Build cascading guardrails with rule priority
Create multiple rules (safety, legal, brand). Assign priority: safety rules run first, brand rules last. Tester shows rule execution order and which rule blocked an output first.
- 4
Use regex patterns for flexible matching
Instead of exact strings, use regex patterns like 'password|api.?key|secret' to catch common sensitive data. Tester shows which patterns matched in your test output.
- 5
Export rules as JSON config for deployment
Once rules are working in tester, export as JSON. Deploy to your LLM server/middleware to enforce guardrails in production. Rules are deterministic, no additional LLM calls needed.