AI-assisted coding is moving into revenue cycle operations faster than most organizations' quality controls can adapt. Coders, auditors, and compliance leaders are being asked to trust outputs they didn't produce and can't always explain — with real audit, denial, and compliance risk if that trust is misplaced. This session gives attendees a practical, vendor-agnostic framework for evaluating AI coding quality objectively, rather than taking accuracy claims on faith. We'll cover how to define measurable accuracy benchmarks, where human review checkpoints remain non-negotiable, and how to build an audit trail that holds up under payer or compliance scrutiny. We'll also address the operational side: what changes for your team once AI coding tools go live — staffing and QA role shifts, ongoing monitoring cadence, and escalation paths when something goes wrong. Finally, we'll cover how to structure a healthy client-vendor partnership: the right diligence questions before signing, what transparency into model changes should look like, and how to build shared accountability into the relationship rather than a one-way "black box" dependency. Attendees will leave with a working checklist they can apply immediately, whether their organization is evaluating AI coding tools for the first time or already has them in production.