In the rapidly evolving field of AI product development, product managers are tasked with incorporating AI into products by focusing on specialized, domain-specific applications that provide unique value. Successful implementation involves carefully selecting use cases, considering risk and return, especially in enterprise environments, and iteratively improving use cases in production. Establishing a robust evaluation framework with implicit and explicit user feedback, manual expert annotation, and automated evaluations is essential. Best practices include decoupling prompt engineering from development, optimizing for latency alongside accuracy, and not over-optimizing costs early in the product lifecycle. Exploring multiple implementation strategies and learning from existing AI products can help product managers build prototypes and develop intuition for AI capabilities and limitations. Overall, balancing technical capabilities with user needs and maintaining clear quality standards are crucial for successful AI product management.