audit

Run an audit on your AI-generated project artifacts to identify and quantify hallucination, overengineering, and underengineering, producing a structured report mapped to your PRD for actionable remediation.

AI artifact auditing is the process of applying multi-dimensional quality gates to research, plan, and code artifacts to detect hallucination and engineering gaps. You need it before releasing features to identify assumptions and trace them to requirements.

You trace AI codebase findings back to PRD requirements by running an audit that maps hallucination and engineering risks directly to your documented requirements, generating a management-friendly report with clear severities and recommendations.

Yes, the audit applies multi-dimensional quality gates across research, plan, and implementation artifacts, identifying gaps and assumptions while ensuring traceability to your PRD.

The best way to audit AI project artifacts for underengineering gaps is to run a multi-dimensional quality gate assessment that identifies missing functionality, maps findings to PRD requirements, and outputs a stakeholder-ready report with actionable remediation steps.

You should avoid using automated AI artifact audits when you lack defined PRD requirements or baseline project artifacts, as the audit relies on traceability to requirements to accurately quantify hallucination, overengineering, and underengineering risks.