Feature flags and dynamic configuration tools, such as those offered by Split, facilitate the integration, testing, and refinement of AI models like ChatGPT without requiring code redeployment. By allowing real-time experimentation with parameters, prompts, and models, teams can optimize AI-driven experiences safely and efficiently. Implementing AI thoughtfully is crucial, as it enables rapid iteration and evaluation of AI approaches while minimizing risks. Split's platform supports this process by allowing modifications to AI configurations through a user interface, enabling continuous testing and adjustments based on data-driven insights. This approach enhances the flexibility of AI deployments, as teams can test different configurations and training prompts without altering the underlying code, ultimately leading to more effective AI implementations. Split not only provides a robust feature management and experimentation platform but also offers guidance and support to ensure successful feature releases and continuous innovation.