Changing production logic can be daunting due to potential unintended consequences on user behavior, but a method known as "shadow shipping" can mitigate this risk by running old and new code versions in parallel to validate changes with real user data before full deployment. This approach involves executing both code paths simultaneously and logging any differences to identify potential regressions without affecting live operations. By visualizing discrepancies through tools like Datadog, engineers can assess the impact of new logic in real-time, ensuring the new behavior aligns with expectations before toggling feature flags for a full rollout. Although this method incurs slightly higher CPU usage, it offers a significant confidence boost by replacing assumptions with data-driven insights, ultimately transforming feature release anxiety into evidence-based assurance. This practice emphasizes that the best validation occurs in production, where actual user interactions provide richer test data than synthetic environments, and encourages the use of shadow shipping for critical logic changes to ensure parity without disruptions.