The author shares practical insights from his experience building Podscan, a podcast database system that leverages AI for data extraction and analysis. A key takeaway is the importance of having a migration pattern in place for API calls to different AI models. By isolating API interactions into services that can easily switch between old and new models, he mitigates risks associated with updates. For instance, when transitioning from GPT-4.1 to GPT-5, he encountered issues with prompt compatibility. The migration pattern allowed him to run both models side by side, track differences, and revert to a previous version if necessary, ensuring reliability in data handling. Another significant insight involves understanding service tiers offered by AI providers like OpenAI. The author highlights the existence of a lesser-known "Flex" tier, which is billed at half the price of the default tier but may come with slower response times. This discovery prompted him to utilize the Flex tier for backend processes in Podscan, optimizing costs while maintaining data quality. By implementing this strategy, he could double his output without increasing expenses, providing greater value to his customers. Overall, these practices emphasize a structured approach to integrating AI, balancing innovation with cost-efficiency.