Your LLM features will fail in five different ways — adversarial probes (prompt injection, jailbreaks, system-prompt extraction), factual hallucination, instruction-following drift, demographic bias, and toxicity / safety regressions. Traditional QA catches none of them. NORA audits your prompts and endpoints across all five dimensions before you ship and produces a severity-scored report with enforceable policies for the Guardrails layer. One pass. Five dimensions. Done before production finds them.
Map the target to all five quality dimensions
NORA reads the system prompt, endpoint description, and deployment context to identify which dimensions actually apply — adversarial / hallucination / instruction-following / bias / toxicity. A non-decisional summarization tool gets a different audit shape than a regulated triage assistant.
Synthesize probes + evals, predict failures
For each in-scope dimension, NORA generates representative probes — OWASP LLM Top 10 attack variants, claim-extraction evals against tool-output ground truth, counterfactual probe pairs varying protected attributes, NIST-anchored safety probes. Predicts which would surface findings based on what the target prompt actually enforces.
Ship findings as policy rules, hand off to ARIA
Every finding ships as a draft Guardrails policy rule — adversarial → input/output filters, hallucination → grounding requirements, bias → counterfactual-audit triggers, toxicity → refusal + redirect policies. ARIA receives severity counts across all five dimensions to fold into the sprint quality report.